US20040243568A1 - Search engine with natural language-based robust parsing of user query and relevance feedback learning - Google Patents
Search engine with natural language-based robust parsing of user query and relevance feedback learning Download PDFInfo
- Publication number
- US20040243568A1 US20040243568A1 US10/806,789 US80678904A US2004243568A1 US 20040243568 A1 US20040243568 A1 US 20040243568A1 US 80678904 A US80678904 A US 80678904A US 2004243568 A1 US2004243568 A1 US 2004243568A1
- Authority
- US
- United States
- Prior art keywords
- canceled
- user
- query
- search
- search engine
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/31—Indexing; Data structures therefor; Storage structures
- G06F16/313—Selection or weighting of terms for indexing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2452—Query translation
- G06F16/24522—Translation of natural language queries to structured queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F16/00 and subgroups
- G06F2216/03—Data mining
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
- Y10S707/99935—Query augmenting and refining, e.g. inexact access
Definitions
- This invention relates to search engines and other information retrieval tools.
- Search engines have undergone two main evolutions.
- the first evolution produced keyword-based search engines.
- the majority of search engines on the Web today e.g., Yahoo! and MSN.com
- These engines accept a keyword-based query from a user and search in one or more index databases. For instance, a user interested in Chinese restaurants in Seattle may type in “Seattle, Chinese, Restaurants” or a short phrase “Chinese restaurants in Seattle”.
- Keyword-based search engines interpret the user query by focusing only on identifiable keywords (e.g., “restaurant”, “Chinese”, and “Seattle”). Because of its simplicity, the keyword-based search engines can produce unsatisfactory search results, often returning many irrelevant documents (e.g., documents on the Seattle area or restaurants in general). In some cases, the engines return millions of documents in response to a simple keyword query, which often makes it impossible for a user to find the needed information.
- identifiable keywords e.g., “restaurant”, “Chinese”, and “Seattle”. Because of its simplicity, the keyword-based search engines can produce unsatisfactory search results, often returning many irrelevant documents (e.g., documents on the Seattle area or restaurants in general). In some cases, the engines return millions of documents in response to a simple keyword query, which often makes it impossible for a user to find the needed information.
- Keyword-based search engines simply interpret the user query without ascribing any intelligence to the form and expression entered by the user.
- FAQs frequently asked questions
- the second-generation search engines attempt to characterize the user's query in terms of predefined frequently asked questions (FAQs), which are manually indexed from user logs along with corresponding answers.
- FAQs frequently asked questions
- One key characteristic of FAQ searches is that they take advantage of the fact that commonly asked questions are much fewer than total number of questions, and thus can be manually entered. By using user logs, they can compute which questions are most commonly asked.
- FAQ-based search engine is the engine employed at the Web site “Askjeeves.com”.
- the search engine at the site rephrases the question as one or more FAQs, as follows:
- FAQ-based search engines are also limited in their understanding the user's query, because they only look up frequently occurring words in the query, and do not perform any deeper syntactic or semantic analysis.
- the search engine still experiences difficulty locating “Chinese restaurants”, as exemplified by the omission of the modifier “Chinese” in any of the rephrased questions.
- FAQ-based second-generation search engines have improved search precision, there remains a need for further improvement in search engines.
- UI user interface
- keywords-based search engines there are mainly two problems that hinder the discovery of user intention. First, it is not so easy for users to express their intention by simple keywords. Second, keyword-based search engines often return too many results unrelated to the users' intention. For example, a user may want to get travel information about Beijing. Entering ‘travel’ as a keyword query in Yahoo, for example, a user is given 289 categories and 17925 sites and the travel information about Beijing is nowhere in the first 100 items.
- a search engine architecture is designed to handle a full range of user queries, from complex sentence-based queries to simple keyword searches.
- the search engine architecture includes a natural language parser that parses a user query and extracts syntactic and semantic information.
- the parser is robust in the sense that it not only returns fully-parsed results (e.g., a parse tree), but is also capable of returning partially-parsed fragments in those cases where more accurate or descriptive information in the user query is unavailable. This is particularly beneficial in comparison to previous efforts that utilized full parsers (i.e., not robust parsers) in information retrieval. Whereas full parsers tended to fail on many reasonable sentences that were not strictly grammatical, the search engine architecture described herein always returns the best fully-parsed or partially-parsed interpretation possible.
- the search engine architecture has a question matcher to match the fully-parsed output and the partially-parsed fragments to a set of frequently asked questions (FAQs) stored in a database.
- the question matcher correlates the questions with a group of possible answers arranged in standard templates that represent possible solutions to the user query.
- the search engine architecture also has a keyword searcher to locate other possible answers by searching on any keywords returned from the parser.
- the search engine may be configured to search content in databases or on the Web to return possible answers.
- the search engine architecture includes a user interface to facilitate entry of a natural language query and to present the answers returned from the question matcher and the keyword searcher. The user is asked to confirm which answer best represents his/her intentions when entering the initial search query.
- the search engine architecture logs the queries, the answers returned to the user, and the user's confirmation feedback in a log database.
- the search engine has a log analyzer to evaluate the log database and glean information that improves performance of the search engine over time. For instance, the search engine uses the log data to train the parser and the question matcher. As part of this training, the log analyzer is able to derive various weighting factors indicating how relevant a question is to a parsed concept returned from the parser, or how relevant a particular answer is to a particular question. These weighting factors help the search engine obtain results that are more likely to be what the user intended based on the user's query.
- the search engine's ability to identify relevant answers can be statistically measured in terms of a confidence rating.
- the confidence ratings of an accurate and precise search improve with the ability to parse the user query.
- Search results based on a fully-parsed output typically garner the highest confidence rating because the search engine uses essentially most of the information in the user query to discern the user's search intention.
- Search results based on a partially-parsed fragment typically receive a comparatively moderate confidence rating, while search results based on keyword searching are given the lowest confidence rating.
- FIG. 1 is a block diagram of an exemplary computer network in which a server computer implements a search engine for handling client queries.
- FIG. 2 is a block diagram of a search engine architecture.
- FIG. 3 is a flow diagram of a search process using the search engine.
- FIG. 4 is a block diagram of a robust parser employed in the search engine.
- FIG. 5 is a diagrammatic illustration of a tokenization of a Chinese sentence to demonstrate the added difficulties of parsing languages other than English.
- FIG. 6 is a flow diagram of a question matching process employed in the search engine.
- FIG. 7 illustrates database tables used during the question matching process of FIG. 6.
- FIG. 8 illustrates a first screen view of Chinese-version search engine user interface implemented by the search engine.
- FIG. 9 illustrates a second screen view of Chinese-version search engine user interface implemented by the search engine.
- This disclosure describes a search engine architecture that handles a full range of user queries, from complex sentence-based queries to simple keyword searches.
- the architecture includes a natural language parser that parses a user query and extracts syntactic and semantic information.
- the parser is robust in the sense that it not only returns fully-parsed results, but is also capable of returning partially-parsed fragments in those cases where more accurate or descriptive information in the user query is unavailable.
- the search engine architecture interacts with the user for confirmation in terms of the concept the user is asking.
- the query logs are recorded and processed repeatedly, thus providing a powerful language model for the natural language parser as well as for indexing the frequently asked questions and providing relevance-feedback learning capability.
- the search engine architecture is described in the context of an Internet-based system in which a client submits user queries to a server and the server hosts the search engine to conduct the search on behalf of the client.
- the search engine architecture is described as handling English and Chinese languages.
- the architecture may be implemented in other environments and extended to other languages.
- the architecture may be implemented on a proprietary local area network and configured to handle one or more other languages (e.g., Japanese, French, German, etc.).
- FIG. 1 shows an exemplary computer network system 100 in which the search engine architecture may be implemented.
- the network system 100 includes a client computer 102 that submits user queries to a server computer 104 via a network 106 , such as the Internet.
- a network 106 such as the Internet.
- the search engine architecture can be implemented using other networks (e.g., a wide area network or local area network) and should not be limited to the Internet, the architecture will be described in the context of the Internet as one suitable implementation.
- the client 102 is representative of many diverse computer systems, including general-purpose computers (e.g., desktop computer, laptop computer, etc.), network appliances (e.g., set-top box (STB), game console, etc.), and. wireless communication devices (e.g., cellular phones, personal digital assistants (PDAs), pagers, or otherdevices capable of receiving and/or sending wireless data communication).
- general-purpose computers e.g., desktop computer, laptop computer, etc.
- network appliances e.g., set-top box (STB), game console, etc.
- wireless communication devices e.g., cellular phones, personal digital assistants (PDAs), pagers, or otherdevices capable of receiving and/or sending wireless data communication.
- the client 102 includes a processor 110 , a volatile memory 112 (e.g., RAM), a non-volatile memory 114 (e.g., ROM, Flash, hard disk, optical, etc.), one or more input devices 116 (e.g., keyboard, keypad, mouse, remote control, stylus, microphone, etc.) and one or more output devices 118 (e.g., display, audio speakers, etc.).
- a volatile memory 112 e.g., RAM
- non-volatile memory 114 e.g., ROM, Flash, hard disk, optical, etc.
- input devices 116 e.g., keyboard, keypad, mouse, remote control, stylus, microphone, etc.
- output devices 118 e.g., display, audio speakers, etc.
- the client 102 is equipped with a browser 120 , which is stored in non-volatile memory 114 and executed on processor 110 .
- the browser 120 facilitates communication with the server 104 via the network 106 .
- the browser 120 may be configured as a conventional Internet browser that is capable of receiving and rendering documents written in a markup language, such as HTML (hypertext markup language).
- the server 104 implements a search engine architecture that is capable of receiving user queries from the client 102 , parsing the queries to obtain complete phrases, partial phrases, or keywords, and returning the appropriate results.
- the server 104 is representative of many different server environments, including a server for a local area network or wide area network, a backend for such a server, or a Web server. In this latter environment of a Web server, the server 104 may be implemented as one or more computers that are configured with server software to host a site on the Internet 106 , such as a Web site for searching.
- the server 104 has a processor 130 , volatile memory 132 (e.g., RAM), and non-volatile memory 134 (e.g., ROM, Flash, hard disk, optical, RAID memory, etc.).
- volatile memory 132 e.g., RAM
- non-volatile memory 134 e.g., ROM, Flash, hard disk, optical, RAID memory, etc.
- the server 104 runs an operating system 136 and a search engine 140 .
- operating system 136 and search engine 142 are illustrated as discrete blocks stored in the non-volatile memory 134 , although it is recognized that such programs and components reside at various times in different storage components of the server 104 and are executed by the processor 130 .
- these software components are stored in non-volatile memory 134 and from there, are loaded at least partially into the volatile main memory 132 for execution on the processor 130 .
- the search engine 140 includes a robust parser 142 to parse a query using natural language parsing. Depending on the search query, the robust parser produces a fully-parsed output (e.g., a parse tree), one or more partially-parsed fragments, and/or one or more keywords.
- a FAQ matcher 144 matches the fully-parsed output (e.g., a parse tree) and the partially-parsed fragments to a set of possible frequently asked questions that are stored in a database. The FAQ matcher then correlates the questions with a group of possible answers to the user query.
- a keyword searcher 146 attempts to locate other possible answers from conducting keyword searching using the keywords returned from the parser.
- the search engine architecture robustly accommodates many types of user queries, from single keyword strings to full, grammatically correct sentences. If the user enters a complete sentence, the search engine 140 has the ability to parse the sentence for syntactic and semantic information. This information better reveals the user's intention and allows for a more precise search with higher quality results. If the user enters a grammatically incorrect sentence or an incomplete sentence (i.e., a phrase), the search engine 140 attempts to map the partial fragments to FAQ concepts. Finally, even if the user query contains only one or a few search terms, the search engine is able to handle the query as a keyword-based search and return at least some results, albeit not with the same precision and quality.
- the search engine 140 presents the possible answers returned from the FAQ matcher 144 and the keyword searcher 146 to a user. The user is asked to confirm which of the answers best represents the user's intentions in the query. Through this feedback, the search engine may refine the search. Additionally, the search engine may use this relevance feedback to train the architecture in its mapping of a parsed query into relevant answers.
- the search engine includes a query log analyzer 148 that tracks the query, the returned results, and the user's feedback to those results in a log database.
- the query log analyzer 148 analyzes the log database to train the FAQ matcher 144 .
- the query log analyzer 148 is able to derive, over time, various weights indicating how relevant a FAQ is to a parsed concept generated by parsing a particular query, or how relevant a particular answer is to a particular FAQ. These weights help the search engine obtain results that are more likely to be what the user intended based on the user's query.
- the search engine's ability to identify relevant answers can be statistically measured in terms of a confidence rating.
- the confidence ratings of an accurate and precise search improve with the ability to parse the user query.
- Search results based on a fully-parsed output typically garner the highest confidence rating because the search engine uses essentially most of the information in the user query to discern the user's search intention.
- Search results based on a partially-parsed fragment typically receive a comparatively moderate confidence rating, while search results based on keyword searching are given the lowest confidence rating.
- the search engine architecture 140 is formulated according to an underlying premise, referred to as the concept-space hypothesis, that a small subset of concepts cover most user queries. Examples of concepts are: “Finding computer and internet related products and services”, “Finding movies and toys on the Internet”, and so on. It is believed that the first few popular categories will actually cover most of the queries. Upon analyzing a one-day log from MSN.com, the inventors discovered that 30% of the concepts covered approximately 80% of all queries in the selected query pool.
- FIG. 2 illustrates the search engine architecture 140 in more detail. It has a search engine user interface (UI) 200 that seamlessly integrates search functionality and browsing.
- UI search engine user interface
- the search engine UI 200 is served in an HTML document to the client 102 when the client initially addresses the Web site.
- One exemplary implementation of the user interface 200 is described below in more detail beneath the heading “Search Engine User Interface”.
- the user enters a search query via the search engine UI 200 .
- a query string is passed to the natural language-based robust parser 142 , which performs robust is parsing and extracts syntactic as well as semantic information for natural language queries.
- the robust parser 142 includes a natural language parser (NLP) 202 that parses the query string according to rules kept in a rules database 204 .
- the parsed output is ranked with a confidence rating to indicate how likely the output represents the query intensions.
- NLP natural language parser
- the output of the natural language robust parser 142 is a collection of concepts and keywords.
- the concepts are obtained through a semantic analysis and include a fully-parsed output (e.g., a parse tree) and partially-parsed fragments.
- a fully-parsed output e.g., a parse tree
- partially-parsed fragments e.g., a parse tree
- One suitable semantic analysis is described below in the section under the heading “NL-based Robust Parsing”.
- the keywords are either the key phrases extracted directly from the user query or are expanded queries through a synonym table.
- the FAQ matcher 144 has a FAQ matching component 206 that attempts to match the concepts and keywords to predefined frequently asked questions stored in a FAQ database 208 . From the FAQs, the FAQ matching component 206 identifies related templates from a template database 210 that group together similar question parameters. The templates have associated indexed answers that are maintained in an answer database 212 .
- the FAQ matcher 144 effectively maps the parsed concepts and keywords to FAQs, the FAQs to templates, and the templates to answers.
- the FAQ database 208 is configured as a relational database that maintains a set of tables to correlate the concepts, FAQs, templates, and answers.
- One example database structure is described below with reference to FIG. 7.
- the NLP module 142 Concurrent with FAQ-based searching, the NLP module 142 also sends the keywords to a keyword-based module 146 for keyword searching on the user's query.
- the keyword-based module 146 has a meta-search engine 214 that extracts answers from the Web 216 .
- the answers returned from the FAQ matcher 144 and keyword searcher 146 are presented to the user via UI 200 .
- the user is asked to confirm which, if any, of the returned answers best exemplifies the user's intentions in the query.
- the search engine may further refine the search using the confirmed answer as a starting point and return even more accurate results.
- the search engine architecture 140 In addition to facilitating various search levels in an integrated manner, the search engine architecture 140 also supports a query log analyzer 148 that implements methodology to process query logs for the purpose of obtaining new question templates with indexed answers. It also has relevance-feedback capability for improving its indexing and ranking functions. This capability allows the architecture 140 to record users' actions in browsing and selecting the search result, so that the ranking of these results and the importance of each selection can be learned over time.
- the architecture has a log collector 218 to log user actions and system output in a log database 220 .
- Log data mining tools 222 may be used to analyze the log database 220 to glean data used to refine the FAQ database 208 , template database 210 , answer database 212 , and FAQ matching functions 206 .
- a web crawler 224 may also be included to provide information as needed from the Web 216 .
- the search engine architecture 140 may be configured according to COM (Component Object Model) or DCOM (Distributed COM). This allows for design modularity, allowing each individual module to evolve independently from others as long as the inter-module interface remains the same.
- COM Component Object Model
- DCOM Distributed COM
- the search engine architecture 140 offers many benefits, including a higher precision and search efficiency on frequently asked questions. Additionally, the indexed contents evolve with users' current interests and its ranking ability improves with usage over time.
- the search engine architecture scales easily to offer relatively large coverage for user's questions and the natural user interface allow users to seamlessly integrate search and browsing.
- FIG. 3 shows a search process 300 conducted on the search engine architecture 140 of FIG. 2.
- the search process 300 is implemented as computer executable instructions that, when executed, perform the operations illustrated as blocks in FIG. 3. Selected operations of the search process 300 are described after this section in more detail.
- the search engine 140 receives a user query entered at remote client 102 .
- the user query is parsed at the natural language robust parser 142 to produce the parsed concepts (if any) and keywords.
- the concepts and keywords are submitted to the FAQ matcher 144 to match them with frequently asked questions in the FAQ database (block 306 ).
- the FAQ matcher 144 identifies associated templates with indexed answers from databases 210 and 212 to obtain answers for the user queries (block 308 ).
- the search engine Concurrent to the FAQ-matching operations, the search engine also performs a keyword search at keyword-based module 146 (block 310 ). At block 312 , the results of the FAQ matching and keyword searching are presented to the user via the search engine UI 200 . The user is then given the opportunity to offer feedback in an attempt to confirm the accuracy of the search.
- the search engine is also providing relevance feedback learning through analysis of the query, the returned results and the user feedback to the search results.
- the log collector 218 logs user queries, results returned to the user, and selections made by the user. These records are stored in the log database 220 .
- the log database 220 is analyzed to ascertain frequently asked questions from a large number of user questions and to automatically develop or find answers for the questions.
- the log is further analyzed to determine weights indicating how probable the returned results pertain to the users' queries (block 318 ).
- the log analyzer determines how likely the FAQs represent the user queries and how likely the answers pertain to the FAQs.
- the weightings are used to modify the FAQ matcher 144 (block 320 ).
- the natural language-based robust parser 142 employs robust parsing to accommodate many diverse types of user queries, including full and partial sentences, meaningful phrases, and independent search terms.
- User queries are often entered into search engines as incomplete or grammatically incorrect sentences. For instance, users who want to know about Chinese restaurants in Seattle might enter queries quite differently, as illustrated by the following examples:
- the robust parser 142 is capable of handling such partial or grammatically incorrect sentences. Unlike traditional parsing that require a hypothesis and a partial parse to cover adjacent words in the input, robust parsing relaxes this requirement, making it possible to omit noisy words in the input. If a user query contains words that are not parsable, the natural language parsing module 142 can skip these words or phrases and still output a result.
- FIG. 4 shows an exemplary implementation of the natural language robust parser 142 .
- the module includes a word segmentation unit 400 , which identifies individual words in a sentence.
- the word segmentation unit 400 relies on data from a query log 402 and a dictionary 404 .
- words are separated by spaces and hence, word segmentation is easily accomplished.
- segmentation is not a trivial task.
- Chinese text for example, there is no separator between words.
- a sequence of characters may have many possible parses in the word-tokenization stage. Thus, effective information retrieval of Chinese first requires good word segmentation.
- FIG. 5 shows an example tokenization 500 of a simple Chinese sentence “ ”, having only four characters.
- these four characters can be parsed in five ways into words.
- the dotted path 502 represents a parsing to the phrase “dismounted a horse”, and the bold path 504 represents “immediately coming down”.
- This figure also shows seven possible “words”, some of which (e.g., ) might be disputable on whether they should be considered “words.”
- the robust parser can accept two kinds of input: Lattice and N-best.
- the lattice input includes almost all possible segmentations. However, as there may be too much ambiguity, the parsing process can become very slow.
- An alternative choice is to use the N-best input.
- the segmented sentence to is passed a natural language parser 410 and a keyword modules.
- the parser 410 attempts to parse the segmented sentence according to a set of rules found in a rule database 414 . If a sentence parses successfully, the parsing module 412 outputs a parse tree. If parsing is unsuccessful, the keyword unit 412 uses a word database 416 to extract and output keywords from the segmented sentence. As shown in FIG. 2, the parse tree and keywords are passed to the FAQ matcher 144 and the keywords are passed to the keyword-based component 146 . Accordingly, the architecture 140 allows templates to be matched regardless of the type of is output, whether parse trees or keywords.
- LEAP spoken language system
- LEAP is technology being developed in Microsoft Research that aims at spoken language understanding.
- Y. Wang entitled “A robust parser for spoken language understanding”, Proc. of 6 th European conference on speech communication and technology ( Eurospeech 99), Budapest, Hungary, September 1999, pp. Vol. 5, 2055-2058.
- the robust parser employs a parsing algorithm that is an extension of a bottom-up chart-parsing algorithm.
- TravelPath is a semantic class that contains a number of rules (the first line) and productions (the second line).
- “@from” parses a piece of the input sentence according to a production as shown in the second line.
- the input item after the “@from” object matches according to a ⁇ PlaceName> semantic class. If there are input tokens that are not parsable by any parts of the rule, it will be ignored by the parser. In this case, the scoring of the parse result will be correspondingly discounted to reflect a lower level of confidence in the parse result.
- ⁇ VOID> represents the root semantic class. Note that this input query cannot be parsed using the first rule in the semantic class TravelPath if a traditional parser is used because the Chinese word “ ” cannot match any objects in the rule. Since the robust parser can skip this word to match the rest, parsing will continue to produce a partial result.
- the score of the parsing result is calculated by discounting the number of input items and rule items that are skipped during the parsing operation. This score is normalized to give a percentage confidence value.
- a parsed result will be selected if it covers the most words in the query and the most parts of rules.
- the search engine learns probabilities from query logs, including:
- the search engine can train the probabilities associated with this rule.
- a rule with a high probability value means that using the rule to parse a query is more reliable.
- the search engine can also train the penalty values for robust matching by exacting a penalty for any item in either a rule or the query sentence that is skipped during parsing.
- the FAQ matcher 144 attempts to find a set of relevant concepts and their related answers from a given user query. To accomplish this, the FAQ matcher 144 maps the concepts through several intermediate spaces to ultimately identify answers to the queries.
- FIG. 6 shows a mapping process 600 of the question matching operation.
- the mapping process 600 is implemented as computer executable instructions that, when executed, perform the operations illustrated as blocks in FIG. 6.
- the mapping process is described in the context of a realistic example in which a user asks:
- the FAQ matcher maps the parsed query from a query space to a concept or FAQ space.
- the natural language processing module 142 returns a parse tree containing a semantic class and its parameters:
- a collection of concepts indexed on ” ” (“Route”) and “ ” (“Travel”), and possibly other related concepts, are stored in the FAQ database 208 .
- FIG. 7 illustrates example database tables 700 maintained in the FAQ database 208 .
- the FAQ database is configured as a relational database in which data records are organized in tables that may be associated with one another using definable relationships.
- the database includes a Concept-FAQ table 702 , a FAQ table 704 , a template table 706 , and an answer table 708 .
- the answer table 708 pertains to answers about a flight schedule, and hence is labeled as a “Flight Table”.
- the Concept-FAQ table 702 is the core data structure for the whole database. It correlates concepts with frequently asked questions (FAQs).
- a FAQ is made up of a few concepts that are in fact represented by certain terms, such as “Route”. Every FAQ is related to one or more concepts and every concept is related to one or more FAQs. Thus, there is a many-to-many relationship between FAQs and concepts. Every FAQ is assigned a FAQ ID to uniquely distinguish FAQs from one another.
- a record in the Concept-FAQ table 702 includes a concept, a FAQ ID, and a weight.
- Each record indicates that a FAQ (with a particular ID) is related to the concept according to a correlation weighting factor.
- the weighting factor indicates how probable the concept pertains to the associated FAQ.
- the weighting factor is learned from a later analysis of the query log file.
- the FAQ matcher can obtain the top n best-matched FAQs.
- the concept set of the question “ ” (“How to go from Beijing to Shanghai”) are “Travel” and “Route”, where the match result is a FAQ set ⁇ 101 (weight 165), 105(weight 90) ⁇ .
- the semantic class returned from the parser is used to search the concept-FAQ table.
- the semantic class “Route” is used as a key to search the Concept-FAQ table 702 .
- the search determines that the third entry 710 in the table yields a perfect match.
- the FAQ matcher maps the FAQs from the FAQ space to a template space.
- a template represents a class of standard questions and corresponds to a semantic class in the robust parser. Every template has one or more parameters with values. Once all the parameters in a template are assigned a value, a standard question is derived from this template.
- the FAQ table 704 associates frequently asked questions with templates.
- the FAQ table 704 may also include a weight to indicate how likely a FAQ pertains to a template.
- the frequently asked question with an ID of “101” has three entries in the FAQ table 704 , identifying three corresponding templates with IDs 18 , 21 and 24 .
- Template 24 carries a weight of “100”, indicating that this template is perhaps a better fit for the given FAQ than the other templates.
- the template IDs can then be used to index into the template table 706 .
- the template table 706 correlates template IDs with template descriptions and identities of corresponding answer sets.
- the template with ID 18 corresponds to an answer table that is named “Flight Table.”
- the mapping result for FAQ set ⁇ 101, 105 ⁇ is a template set ⁇ 24( weight 165+100), 18( weight 165+80), 21( weight 165+50), 31( weight 90+75) ⁇ , where the weights are obtained by a simple addition of the weights from previous steps.
- the FAQ matcher maps, templates from the template space to an answer space. All answers for a template are previously stored in a separate answer table, such as answer table 708 .
- the answer table is indexed by parameter values of the template. When matching is done, the best parameter is calculated and passed to the search engine UI 200 to be shown to the user.
- every answer has two parts: a URL and its description.
- a URL a template 18 ( )
- value of the parameter is assigned to “ ”
- the flight table is returned with the portion of “ ” in the table shown to the user.
- the search engine architecture 140 uses information mined by the log analyzer 148 to adapt the robust parser 142 so that it evaluates the output based on the coverage of a rule against the input query. A parsed result will be selected if it covers the most words in the query and the most parts of rules.
- probabilities learned from query logs include:
- the productions in grammar are either global or local to a semantic class.
- the probabilities for all global productions (the productions always available) that expand the same item sum to one.
- the probabilities for all productions local to one semantic class (the productions only available within a semantic class) that expand the same item sum to one too.
- the next task is to learn the confidence values associated with each item in a rule._Considering a rule having N items, robust matching is performed on the rule. Suppose the items T i 1 , T i 2 , K T i m are matched, but the items T j 1 , T j 2 K T j n (1 ⁇ i l , j k ⁇ N) are not matched. A confidence value indicating how well this rule is matched is then measured. The measurement may be performed, for example, by using neural networks.
- a perceptron has N input units, each of them representing an item in the rule, and one output unit, which represents the confidence of the rule matching.
- a Sigmoid function is used as the activation function for the output unit.
- w tp is the weight from input unit I p to output unit.
- a standard gradient descent method is used to train the perceptron, such as that described in S. Russell, P. Norvig, “Artificial Intelligence”, Prentice-Hall, Inc. 1995, pp 573-577.
- the training data is the user query log file where the sentences are classified as positive and negative examples.
- the last task is to learn the confidence values associated with each word in an input sentence.
- a non-matching word is the word in the input sentence that does not match any item in the rule.
- the search engine UI 200 is designed to improve efficiency and accuracy in information retrieval based on a user's search intention.
- the intention-centric UI design guides users to a small number of high-quality results, often consisting of fewer than ten intention-related answers.
- the “intention” of a search on the Internet is a process rather than an event.
- the search engine UI 200 attempts to capture the process as three main tasks. First, users are permitted to pose queries as natural language questions. Second, the UI presents parameterized search results from the search engine and asks users to confirm their intention. Finally, users are permitted to select their desired answer.
- FIG. 8 shows an example screen display 800 of the search engine UI 200 .
- the screen display has a query entry area 802 that allows user to enter natural language questions.
- query entry area 802 that allows user to enter natural language questions.
- Natural language is a powerful tool for expressing the user intention.
- the most important parts of a query are referred to as core phrases.
- the underlined words are core phrases
- the parenthesized words are keywords
- the remaining words are redundant words.
- search engine selects all possibly relevant concept templates and asks the user to confirm. Related concepts are clustered according to their similarity and the different parts of the result are treated as parameters. From the above query, two similar search results “ ” (“famous sites in Beijing”) and “; ” (“famous sites in Shanghai”) are combined into one group, where (Beijing) and (Shanghai) are treated as parameters.
- FIG. 9 shows an exemplary display screen 900 that is returned with various parameterized search results.
- the result “( ) ” (famous sites in [Beijing
- the parameterized result can help focus users' attention on the core phrases, which in this case corresponds to “ ” (famous sites).
- the search engine UI is designed to seamlessly integrate searching and browsing.
- the search engine UI is constructed with a strong sense of structure and navigation support so that users know where they are, where they have been, and where they can go.
- the user may wish to search first, rather than browse to a travel web site.
- the search engine judges the user intention by using the core phrases. Because the intention extends beyond a simple question, the search engine predicts the user's intention from the current query and provides reasonable answers for confirmation. For example, in the above example, the real goal of the user is to get useful information about traveling to Shanghai. Thus, the sightseeing information about Shanghai is related to the user's intention.
- the search results are two alternative answers related to the user's intention:
- a new-generation search engine for Internet searching permits natural language understanding, FAQ template database matching and user interface components.
- the architecture is configured to precisely index frequently asked concepts and intentions from user queries, based on parsed results and/or keywords.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US10/806,789 US20040243568A1 (en) | 2000-08-24 | 2004-03-22 | Search engine with natural language-based robust parsing of user query and relevance feedback learning |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US09/645,806 US6766320B1 (en) | 2000-08-24 | 2000-08-24 | Search engine with natural language-based robust parsing for user query and relevance feedback learning |
US10/806,789 US20040243568A1 (en) | 2000-08-24 | 2004-03-22 | Search engine with natural language-based robust parsing of user query and relevance feedback learning |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US09/645,806 Division US6766320B1 (en) | 2000-08-24 | 2000-08-24 | Search engine with natural language-based robust parsing for user query and relevance feedback learning |
Publications (1)
Publication Number | Publication Date |
---|---|
US20040243568A1 true US20040243568A1 (en) | 2004-12-02 |
Family
ID=32682766
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US09/645,806 Expired - Lifetime US6766320B1 (en) | 2000-08-24 | 2000-08-24 | Search engine with natural language-based robust parsing for user query and relevance feedback learning |
US10/806,789 Abandoned US20040243568A1 (en) | 2000-08-24 | 2004-03-22 | Search engine with natural language-based robust parsing of user query and relevance feedback learning |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US09/645,806 Expired - Lifetime US6766320B1 (en) | 2000-08-24 | 2000-08-24 | Search engine with natural language-based robust parsing for user query and relevance feedback learning |
Country Status (1)
Country | Link |
---|---|
US (2) | US6766320B1 (US20040243568A1-20041202-P00025.png) |
Cited By (198)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0757798A (ja) * | 1993-08-13 | 1995-03-03 | Sumitomo 3M Ltd | 電線接続部の被覆チューブ |
US20020049756A1 (en) * | 2000-10-11 | 2002-04-25 | Microsoft Corporation | System and method for searching multiple disparate search engines |
US20030144994A1 (en) * | 2001-10-12 | 2003-07-31 | Ji-Rong Wen | Clustering web queries |
US20040158560A1 (en) * | 2003-02-12 | 2004-08-12 | Ji-Rong Wen | Systems and methods for query expansion |
US20040254917A1 (en) * | 2003-06-13 | 2004-12-16 | Brill Eric D. | Architecture for generating responses to search engine queries |
US20050203934A1 (en) * | 2004-03-09 | 2005-09-15 | Microsoft Corporation | Compression of logs of language data |
US20060085401A1 (en) * | 2004-10-20 | 2006-04-20 | Microsoft Corporation | Analyzing operational and other data from search system or the like |
US7089218B1 (en) * | 2004-01-06 | 2006-08-08 | Neuric Technologies, Llc | Method for inclusion of psychological temperament in an electronic emulation of the human brain |
US20060184517A1 (en) * | 2005-02-15 | 2006-08-17 | Microsoft Corporation | Answers analytics: computing answers across discrete data |
US20060184529A1 (en) * | 2005-02-16 | 2006-08-17 | Gal Berg | System and method for analysis and management of logs and events |
US20060206476A1 (en) * | 2005-03-10 | 2006-09-14 | Yahoo!, Inc. | Reranking and increasing the relevance of the results of Internet searches |
US20070005343A1 (en) * | 2005-07-01 | 2007-01-04 | Xerox Corporation | Concept matching |
US20070025528A1 (en) * | 2005-07-07 | 2007-02-01 | Sbc Knowledge Ventures, L.P. | System and method for automated performance monitoring for a call servicing system |
US20070143278A1 (en) * | 2005-12-15 | 2007-06-21 | Microsoft Corporation | Context-based key phrase discovery and similarity measurement utilizing search engine query logs |
US20070156625A1 (en) * | 2004-01-06 | 2007-07-05 | Neuric Technologies, Llc | Method for movie animation |
US20070192313A1 (en) * | 2006-01-27 | 2007-08-16 | William Derek Finley | Data search method with statistical analysis performed on user provided ratings of the initial search results |
US20070208724A1 (en) * | 2006-03-06 | 2007-09-06 | Anand Madhavan | Vertical search expansion, disambiguation, and optimization of search queries |
US20070208706A1 (en) * | 2006-03-06 | 2007-09-06 | Anand Madhavan | Vertical search expansion, disambiguation, and optimization of search queries |
WO2007108788A2 (en) * | 2006-03-13 | 2007-09-27 | Answers Corporation | Method and system for answer extraction |
US20070250464A1 (en) * | 2004-01-06 | 2007-10-25 | Neuric Technologies, Llc | Historical figures in today's society |
US20070282765A1 (en) * | 2004-01-06 | 2007-12-06 | Neuric Technologies, Llc | Method for substituting an electronic emulation of the human brain into an application to replace a human |
US20070288406A1 (en) * | 2004-01-06 | 2007-12-13 | Neuric Technologies, Llc | Method for determining relationships through use of an ordered list between processing nodes in an emulated human brain |
US20080022211A1 (en) * | 2006-07-24 | 2008-01-24 | Chacha Search, Inc. | Method, system, and computer readable storage for podcasting and video training in an information search system |
US20080082485A1 (en) * | 2006-09-28 | 2008-04-03 | Microsoft Corporation | Personalized information retrieval search with backoff |
US20080104047A1 (en) * | 2005-02-16 | 2008-05-01 | Transaxtions Llc | Intelligent search with guiding info |
US20080195378A1 (en) * | 2005-02-08 | 2008-08-14 | Nec Corporation | Question and Answer Data Editing Device, Question and Answer Data Editing Method and Question Answer Data Editing Program |
US20080228467A1 (en) * | 2004-01-06 | 2008-09-18 | Neuric Technologies, Llc | Natural language parsing method to provide conceptual flow |
US20080243741A1 (en) * | 2004-01-06 | 2008-10-02 | Neuric Technologies, Llc | Method and apparatus for defining an artificial brain via a plurality of concept nodes connected together through predetermined relationships |
US20080255957A1 (en) * | 2007-04-16 | 2008-10-16 | Ebay Inc, | System and method for online item publication and marketplace within virtual worlds |
US20080256040A1 (en) * | 2007-04-16 | 2008-10-16 | Ebay Inc. | Visualization of reputation ratings |
US20090276469A1 (en) * | 2008-05-01 | 2009-11-05 | International Business Machines Corporation | Method for transactional behavior extaction in distributed applications |
US20090307194A1 (en) * | 2005-06-03 | 2009-12-10 | Delefevre Patrick Y | Neutral sales consultant |
US7657005B2 (en) | 2004-11-02 | 2010-02-02 | At&T Intellectual Property I, L.P. | System and method for identifying telephone callers |
US7668889B2 (en) | 2004-10-27 | 2010-02-23 | At&T Intellectual Property I, Lp | Method and system to combine keyword and natural language search results |
US20100088262A1 (en) * | 2008-09-29 | 2010-04-08 | Neuric Technologies, Llc | Emulated brain |
US20100114878A1 (en) * | 2008-10-22 | 2010-05-06 | Yumao Lu | Selective term weighting for web search based on automatic semantic parsing |
US7716229B1 (en) | 2006-03-31 | 2010-05-11 | Microsoft Corporation | Generating misspells from query log context usage |
US7720203B2 (en) | 2004-12-06 | 2010-05-18 | At&T Intellectual Property I, L.P. | System and method for processing speech |
US7724889B2 (en) | 2004-11-29 | 2010-05-25 | At&T Intellectual Property I, L.P. | System and method for utilizing confidence levels in automated call routing |
US20100145923A1 (en) * | 2008-12-04 | 2010-06-10 | Microsoft Corporation | Relaxed filter set |
US20100153094A1 (en) * | 2008-12-11 | 2010-06-17 | Electronics And Telecommunications Research Institute | Topic map based indexing and searching apparatus |
US7751551B2 (en) | 2005-01-10 | 2010-07-06 | At&T Intellectual Property I, L.P. | System and method for speech-enabled call routing |
US20100185437A1 (en) * | 2005-01-06 | 2010-07-22 | Neuric Technologies, Llc | Process of dialogue and discussion |
CN101808003A (zh) * | 2010-02-11 | 2010-08-18 | 候万春 | 提供即时类型通信的客户端和系统以及方法 |
US20100306214A1 (en) * | 2009-05-28 | 2010-12-02 | Microsoft Corporation | Identifying modifiers in web queries over structured data |
US7864942B2 (en) | 2004-12-06 | 2011-01-04 | At&T Intellectual Property I, L.P. | System and method for routing calls |
US7933399B2 (en) | 2005-03-22 | 2011-04-26 | At&T Intellectual Property I, L.P. | System and method for utilizing virtual agents in an interactive voice response application |
US7936861B2 (en) | 2004-07-23 | 2011-05-03 | At&T Intellectual Property I, L.P. | Announcement system and method of use |
US7966176B2 (en) | 2005-01-14 | 2011-06-21 | At&T Intellectual Property I, L.P. | System and method for independently recognizing and selecting actions and objects in a speech recognition system |
US8005204B2 (en) | 2005-06-03 | 2011-08-23 | At&T Intellectual Property I, L.P. | Call routing system and method of using the same |
US20110246496A1 (en) * | 2008-12-11 | 2011-10-06 | Chung Hee Sung | Information search method and information provision method based on user's intention |
US20110276535A1 (en) * | 2010-05-05 | 2011-11-10 | Salesforce.Com, Inc. | Knowledge article workflow management |
US8068596B2 (en) | 2005-02-04 | 2011-11-29 | At&T Intellectual Property I, L.P. | Call center system for multiple transaction selections |
US8090086B2 (en) | 2003-09-26 | 2012-01-03 | At&T Intellectual Property I, L.P. | VoiceXML and rule engine based switchboard for interactive voice response (IVR) services |
US8102992B2 (en) | 2004-10-05 | 2012-01-24 | At&T Intellectual Property, L.P. | Dynamic load balancing between multiple locations with different telephony system |
US8130936B2 (en) | 2005-03-03 | 2012-03-06 | At&T Intellectual Property I, L.P. | System and method for on hold caller-controlled activities and entertainment |
US20120059816A1 (en) * | 2010-09-07 | 2012-03-08 | Priyesh Narayanan | Building content in q&a sites by auto-posting of questions extracted from web search logs |
US8165281B2 (en) | 2004-07-28 | 2012-04-24 | At&T Intellectual Property I, L.P. | Method and system for mapping caller information to call center agent transactions |
US20120150836A1 (en) * | 2010-12-08 | 2012-06-14 | Microsoft Corporation | Training parsers to approximately optimize ndcg |
US8223954B2 (en) | 2005-03-22 | 2012-07-17 | At&T Intellectual Property I, L.P. | System and method for automating customer relations in a communications environment |
US8244726B1 (en) | 2004-08-31 | 2012-08-14 | Bruce Matesso | Computer-aided extraction of semantics from keywords to confirm match of buyer offers to seller bids |
US8280030B2 (en) | 2005-06-03 | 2012-10-02 | At&T Intellectual Property I, Lp | Call routing system and method of using the same |
US8295469B2 (en) | 2005-05-13 | 2012-10-23 | At&T Intellectual Property I, L.P. | System and method of determining call treatment of repeat calls |
US8401851B2 (en) | 2004-08-12 | 2013-03-19 | At&T Intellectual Property I, L.P. | System and method for targeted tuning of a speech recognition system |
CN102999496A (zh) * | 2011-09-09 | 2013-03-27 | 北京百度网讯科技有限公司 | 建立需求分析模板的方法、搜索需求识别的方法及装置 |
US8463720B1 (en) | 2009-03-27 | 2013-06-11 | Neuric Technologies, Llc | Method and apparatus for defining an artificial brain via a plurality of concept nodes defined by frame semantics |
US8489525B2 (en) | 2010-05-20 | 2013-07-16 | International Business Machines Corporation | Automatic model evolution |
US8526577B2 (en) | 2005-08-25 | 2013-09-03 | At&T Intellectual Property I, L.P. | System and method to access content from a speech-enabled automated system |
US8548157B2 (en) | 2005-08-29 | 2013-10-01 | At&T Intellectual Property I, L.P. | System and method of managing incoming telephone calls at a call center |
US20130275121A1 (en) * | 2005-08-01 | 2013-10-17 | Evi Technologies Limited | Knowledge repository |
US8655901B1 (en) | 2010-06-23 | 2014-02-18 | Google Inc. | Translation-based query pattern mining |
US20140058724A1 (en) * | 2012-07-20 | 2014-02-27 | Veveo, Inc. | Method of and System for Using Conversation State Information in a Conversational Interaction System |
US8731165B2 (en) | 2005-07-01 | 2014-05-20 | At&T Intellectual Property I, L.P. | System and method of automated order status retrieval |
US20140188477A1 (en) * | 2012-12-31 | 2014-07-03 | Via Technologies, Inc. | Method for correcting a speech response and natural language dialogue system |
WO2014159187A2 (en) * | 2013-03-14 | 2014-10-02 | Worldone, Inc. | System and method for concept discovery with online information environments |
US20150019202A1 (en) * | 2013-07-15 | 2015-01-15 | Nuance Communications, Inc. | Ontology and Annotation Driven Grammar Inference |
US8938438B2 (en) * | 2012-10-11 | 2015-01-20 | Go Daddy Operating Company, LLC | Optimizing search engine ranking by recommending content including frequently searched questions |
US20150039597A1 (en) * | 2013-07-30 | 2015-02-05 | Facebook, Inc. | Rewriting Search Queries on Online Social Networks |
US8977624B2 (en) | 2010-08-30 | 2015-03-10 | Microsoft Technology Licensing, Llc | Enhancing search-result relevance ranking using uniform resource locators for queries containing non-encoding characters |
US20150142851A1 (en) * | 2013-11-18 | 2015-05-21 | Google Inc. | Implicit Question Query Identification |
US20150213462A1 (en) * | 2014-01-24 | 2015-07-30 | Go Daddy Operating Company, LLC | Highlighting business trends |
US20150220608A1 (en) * | 2012-05-25 | 2015-08-06 | International Business Machines Corporation | Providing search query results based on entity variant generation and normalization |
US9110882B2 (en) | 2010-05-14 | 2015-08-18 | Amazon Technologies, Inc. | Extracting structured knowledge from unstructured text |
CN104881446A (zh) * | 2015-05-14 | 2015-09-02 | 百度在线网络技术(北京)有限公司 | 搜索方法及装置 |
WO2015138497A3 (en) * | 2014-03-10 | 2015-12-03 | Interana, Inc. | Systems and methods for rapid data analysis |
US9218390B2 (en) | 2011-07-29 | 2015-12-22 | Yellowpages.Com Llc | Query parser derivation computing device and method for making a query parser for parsing unstructured search queries |
US20160078012A1 (en) * | 2014-09-11 | 2016-03-17 | Bmc Software, Inc. | Systems and methods for formless information technology and social support mechanics |
US9336297B2 (en) * | 2012-08-02 | 2016-05-10 | Paypal, Inc. | Content inversion for user searches and product recommendations systems and methods |
US9342623B2 (en) | 2010-04-19 | 2016-05-17 | Facebook, Inc. | Automatically generating nodes and edges in an integrated social graph |
US20160210301A1 (en) * | 2009-02-13 | 2016-07-21 | Microsoft Technology Licensing, Llc | Context-Aware Query Suggestion by Mining Log Data |
US9465848B2 (en) | 2010-04-19 | 2016-10-11 | Facebook, Inc. | Detecting social graph elements for structured search queries |
US9465878B2 (en) | 2014-01-17 | 2016-10-11 | Go Daddy Operating Company, LLC | System and method for depicting backlink metrics for a website |
US9465833B2 (en) | 2012-07-31 | 2016-10-11 | Veveo, Inc. | Disambiguating user intent in conversational interaction system for large corpus information retrieval |
US9514218B2 (en) | 2010-04-19 | 2016-12-06 | Facebook, Inc. | Ambiguous structured search queries on online social networks |
US9519681B2 (en) | 2007-10-04 | 2016-12-13 | Amazon Technologies, Inc. | Enhanced knowledge repository |
US9536522B1 (en) * | 2013-12-30 | 2017-01-03 | Google Inc. | Training a natural language processing model with information retrieval model annotations |
US9594852B2 (en) | 2013-05-08 | 2017-03-14 | Facebook, Inc. | Filtering suggested structured queries on online social networks |
US9602965B1 (en) | 2015-11-06 | 2017-03-21 | Facebook, Inc. | Location-based place determination using online social networks |
CN106682210A (zh) * | 2016-12-30 | 2017-05-17 | 广州华多网络科技有限公司 | 日志文件查询方法及装置 |
US9697259B1 (en) | 2009-08-31 | 2017-07-04 | Google Inc. | Refining search results |
US9715596B2 (en) | 2013-05-08 | 2017-07-25 | Facebook, Inc. | Approximate privacy indexing for search queries on online social networks |
US9720956B2 (en) | 2014-01-17 | 2017-08-01 | Facebook, Inc. | Client-side search templates for online social networks |
US9753992B2 (en) | 2013-07-30 | 2017-09-05 | Facebook, Inc. | Static rankings for search queries on online social networks |
US9753993B2 (en) | 2012-07-27 | 2017-09-05 | Facebook, Inc. | Social static ranking for search |
US9805089B2 (en) | 2009-02-10 | 2017-10-31 | Amazon Technologies, Inc. | Local business and product search system and method |
US9811566B1 (en) * | 2006-11-02 | 2017-11-07 | Google Inc. | Modifying search result ranking based on implicit user feedback |
US9852136B2 (en) | 2014-12-23 | 2017-12-26 | Rovi Guides, Inc. | Systems and methods for determining whether a negation statement applies to a current or past query |
US9854049B2 (en) | 2015-01-30 | 2017-12-26 | Rovi Guides, Inc. | Systems and methods for resolving ambiguous terms in social chatter based on a user profile |
CN107885874A (zh) * | 2017-11-28 | 2018-04-06 | 上海智臻智能网络科技股份有限公司 | 数据查询方法和装置、计算机设备及计算机可读存储介质 |
US9959318B2 (en) | 2010-04-19 | 2018-05-01 | Facebook, Inc. | Default structured search queries on online social networks |
US10019466B2 (en) | 2016-01-11 | 2018-07-10 | Facebook, Inc. | Identification of low-quality place-entities on online social networks |
US10026021B2 (en) | 2016-09-27 | 2018-07-17 | Facebook, Inc. | Training image-recognition systems using a joint embedding model on online social networks |
US10032186B2 (en) | 2013-07-23 | 2018-07-24 | Facebook, Inc. | Native application testing |
CN108491506A (zh) * | 2018-03-22 | 2018-09-04 | 上海连尚网络科技有限公司 | 用于推送问题答案组合的方法 |
US10083379B2 (en) | 2016-09-27 | 2018-09-25 | Facebook, Inc. | Training image-recognition systems based on search queries on online social networks |
US10102255B2 (en) | 2016-09-08 | 2018-10-16 | Facebook, Inc. | Categorizing objects for queries on online social networks |
US10102245B2 (en) | 2013-04-25 | 2018-10-16 | Facebook, Inc. | Variable search query vertical access |
US10121493B2 (en) | 2013-05-07 | 2018-11-06 | Veveo, Inc. | Method of and system for real time feedback in an incremental speech input interface |
US10129705B1 (en) | 2017-12-11 | 2018-11-13 | Facebook, Inc. | Location prediction using wireless signals on online social networks |
US10140338B2 (en) | 2010-04-19 | 2018-11-27 | Facebook, Inc. | Filtering structured search queries based on privacy settings |
US10146835B2 (en) | 2016-08-23 | 2018-12-04 | Interana, Inc. | Methods for stratified sampling-based query execution |
US20180357282A1 (en) * | 2017-06-12 | 2018-12-13 | KMS Lighthouse Ltd. | System and method for efficiently handling queries |
US10157224B2 (en) | 2016-02-03 | 2018-12-18 | Facebook, Inc. | Quotations-modules on online social networks |
US10162899B2 (en) | 2016-01-15 | 2018-12-25 | Facebook, Inc. | Typeahead intent icons and snippets on online social networks |
US10162886B2 (en) | 2016-11-30 | 2018-12-25 | Facebook, Inc. | Embedding-based parsing of search queries on online social networks |
US10185763B2 (en) | 2016-11-30 | 2019-01-22 | Facebook, Inc. | Syntactic models for parsing search queries on online social networks |
US10216850B2 (en) | 2016-02-03 | 2019-02-26 | Facebook, Inc. | Sentiment-modules on online social networks |
US20190065602A1 (en) * | 2010-09-10 | 2019-02-28 | Veveo, Inc. | Method of and system for conducting personalized federated search and presentation of results therefrom |
US10223464B2 (en) | 2016-08-04 | 2019-03-05 | Facebook, Inc. | Suggesting filters for search on online social networks |
US10235469B2 (en) | 2016-11-30 | 2019-03-19 | Facebook, Inc. | Searching for posts by related entities on online social networks |
US10244042B2 (en) | 2013-02-25 | 2019-03-26 | Facebook, Inc. | Pushing suggested search queries to mobile devices |
US10242074B2 (en) | 2016-02-03 | 2019-03-26 | Facebook, Inc. | Search-results interfaces for content-item-specific modules on online social networks |
US10248645B2 (en) | 2017-05-30 | 2019-04-02 | Facebook, Inc. | Measuring phrase association on online social networks |
US10262039B1 (en) | 2016-01-15 | 2019-04-16 | Facebook, Inc. | Proximity-based searching on online social networks |
US10268646B2 (en) | 2017-06-06 | 2019-04-23 | Facebook, Inc. | Tensor-based deep relevance model for search on online social networks |
US10270868B2 (en) | 2015-11-06 | 2019-04-23 | Facebook, Inc. | Ranking of place-entities on online social networks |
US10270882B2 (en) | 2016-02-03 | 2019-04-23 | Facebook, Inc. | Mentions-modules on online social networks |
US10282483B2 (en) | 2016-08-04 | 2019-05-07 | Facebook, Inc. | Client-side caching of search keywords for online social networks |
US10296507B2 (en) | 2015-02-12 | 2019-05-21 | Interana, Inc. | Methods for enhancing rapid data analysis |
WO2019100167A1 (en) * | 2017-11-27 | 2019-05-31 | Retailcommon Inc. | Method and system for syntactic searching |
US10313456B2 (en) | 2016-11-30 | 2019-06-04 | Facebook, Inc. | Multi-stage filtering for recommended user connections on online social networks |
US10311117B2 (en) | 2016-11-18 | 2019-06-04 | Facebook, Inc. | Entity linking to query terms on online social networks |
US10331748B2 (en) | 2010-04-19 | 2019-06-25 | Facebook, Inc. | Dynamically generating recommendations based on social graph information |
CN110019713A (zh) * | 2017-12-07 | 2019-07-16 | 上海智臻智能网络科技股份有限公司 | 基于意图理解的数据检索方法和装置、设备及存储介质 |
CN110019712A (zh) * | 2017-12-07 | 2019-07-16 | 上海智臻智能网络科技股份有限公司 | 多意图查询方法和装置、计算机设备及计算机可读存储介质 |
CN110019714A (zh) * | 2017-12-07 | 2019-07-16 | 上海智臻智能网络科技股份有限公司 | 基于历史结果的多意图查询方法、装置、设备及存储介质 |
CN110032631A (zh) * | 2019-03-26 | 2019-07-19 | 腾讯科技(深圳)有限公司 | 一种信息反馈方法、装置和存储介质 |
US10387511B2 (en) | 2015-11-25 | 2019-08-20 | Facebook, Inc. | Text-to-media indexes on online social networks |
US10423387B2 (en) | 2016-08-23 | 2019-09-24 | Interana, Inc. | Methods for highly efficient data sharding |
US10430477B2 (en) | 2010-04-19 | 2019-10-01 | Facebook, Inc. | Personalized structured search queries for online social networks |
US10430426B2 (en) | 2016-05-03 | 2019-10-01 | International Business Machines Corporation | Response effectiveness determination in a question/answer system |
US10452671B2 (en) | 2016-04-26 | 2019-10-22 | Facebook, Inc. | Recommendations from comments on online social networks |
US10489472B2 (en) | 2017-02-13 | 2019-11-26 | Facebook, Inc. | Context-based search suggestions on online social networks |
US10489468B2 (en) | 2017-08-22 | 2019-11-26 | Facebook, Inc. | Similarity search using progressive inner products and bounds |
US10534815B2 (en) | 2016-08-30 | 2020-01-14 | Facebook, Inc. | Customized keyword query suggestions on online social networks |
US10534814B2 (en) | 2015-11-11 | 2020-01-14 | Facebook, Inc. | Generating snippets on online social networks |
US10535106B2 (en) | 2016-12-28 | 2020-01-14 | Facebook, Inc. | Selecting user posts related to trending topics on online social networks |
US10552426B2 (en) | 2017-05-23 | 2020-02-04 | International Business Machines Corporation | Adaptive conversational disambiguation system |
US10579688B2 (en) | 2016-10-05 | 2020-03-03 | Facebook, Inc. | Search ranking and recommendations for online social networks based on reconstructed embeddings |
US10585901B2 (en) | 2015-01-02 | 2020-03-10 | International Business Machines Corporation | Tailoring question answer results to personality traits |
US10586532B1 (en) * | 2019-01-28 | 2020-03-10 | Babylon Partners Limited | Flexible-response dialogue system through analysis of semantic textual similarity |
US10607148B1 (en) | 2016-12-21 | 2020-03-31 | Facebook, Inc. | User identification with voiceprints on online social networks |
US10614141B2 (en) | 2017-03-15 | 2020-04-07 | Facebook, Inc. | Vital author snippets on online social networks |
US10635661B2 (en) | 2016-07-11 | 2020-04-28 | Facebook, Inc. | Keyboard-based corrections for search queries on online social networks |
US10645142B2 (en) | 2016-09-20 | 2020-05-05 | Facebook, Inc. | Video keyframes display on online social networks |
US10650009B2 (en) | 2016-11-22 | 2020-05-12 | Facebook, Inc. | Generating news headlines on online social networks |
US10678786B2 (en) | 2017-10-09 | 2020-06-09 | Facebook, Inc. | Translating search queries on online social networks |
US10685047B1 (en) * | 2016-12-08 | 2020-06-16 | Townsend Street Labs, Inc. | Request processing system |
US10706481B2 (en) | 2010-04-19 | 2020-07-07 | Facebook, Inc. | Personalizing default search queries on online social networks |
US10713242B2 (en) * | 2017-01-17 | 2020-07-14 | International Business Machines Corporation | Enhancing performance of structured lookups using set operations |
US10726022B2 (en) | 2016-08-26 | 2020-07-28 | Facebook, Inc. | Classifying search queries on online social networks |
US10740375B2 (en) | 2016-01-20 | 2020-08-11 | Facebook, Inc. | Generating answers to questions using information posted by users on online social networks |
US10740368B2 (en) | 2015-12-29 | 2020-08-11 | Facebook, Inc. | Query-composition platforms on online social networks |
US10769138B2 (en) | 2017-06-13 | 2020-09-08 | International Business Machines Corporation | Processing context-based inquiries for knowledge retrieval |
US10769222B2 (en) | 2017-03-20 | 2020-09-08 | Facebook, Inc. | Search result ranking based on post classifiers on online social networks |
US10776437B2 (en) | 2017-09-12 | 2020-09-15 | Facebook, Inc. | Time-window counters for search results on online social networks |
US10795886B1 (en) | 2018-03-30 | 2020-10-06 | Townsend Street Labs, Inc. | Dynamic query routing system |
US10795936B2 (en) | 2015-11-06 | 2020-10-06 | Facebook, Inc. | Suppressing entity suggestions on online social networks |
US10810214B2 (en) | 2017-11-22 | 2020-10-20 | Facebook, Inc. | Determining related query terms through query-post associations on online social networks |
US10810217B2 (en) | 2015-10-07 | 2020-10-20 | Facebook, Inc. | Optionalization and fuzzy search on online social networks |
US10817483B1 (en) | 2017-05-31 | 2020-10-27 | Townsend Street Labs, Inc. | System for determining and modifying deprecated data entries |
US10956680B1 (en) * | 2018-10-01 | 2021-03-23 | Knexus Research Corporation | System and method for temporal expressions processing |
US10963514B2 (en) | 2017-11-30 | 2021-03-30 | Facebook, Inc. | Using related mentions to enhance link probability on online social networks |
US11223699B1 (en) | 2016-12-21 | 2022-01-11 | Facebook, Inc. | Multiple user recognition with voiceprints on online social networks |
US11379861B2 (en) | 2017-05-16 | 2022-07-05 | Meta Platforms, Inc. | Classifying post types on online social networks |
WO2022178517A1 (en) * | 2021-02-17 | 2022-08-25 | Iqvia, Inc. | Skipping natural language processor |
US11468050B2 (en) | 2017-11-30 | 2022-10-11 | International Business Machines Corporation | Learning user synonyms from sequenced query sessions |
WO2022187495A3 (en) * | 2021-03-04 | 2022-10-27 | Yext, Inc. | Search experience management system |
US11531707B1 (en) | 2019-09-26 | 2022-12-20 | Okta, Inc. | Personalized search based on account attributes |
US11604968B2 (en) | 2017-12-11 | 2023-03-14 | Meta Platforms, Inc. | Prediction of next place visits on online social networks |
US11803556B1 (en) | 2018-12-10 | 2023-10-31 | Townsend Street Labs, Inc. | System for handling workplace queries using online learning to rank |
US11836069B2 (en) | 2021-02-24 | 2023-12-05 | Open Weaver Inc. | Methods and systems for assessing functional validation of software components comparing source code and feature documentation |
US11836202B2 (en) | 2021-02-24 | 2023-12-05 | Open Weaver Inc. | Methods and systems for dynamic search listing ranking of software components |
US11853745B2 (en) | 2021-02-26 | 2023-12-26 | Open Weaver Inc. | Methods and systems for automated open source software reuse scoring |
US11893385B2 (en) | 2021-02-17 | 2024-02-06 | Open Weaver Inc. | Methods and systems for automated software natural language documentation |
US11921763B2 (en) | 2021-02-24 | 2024-03-05 | Open Weaver Inc. | Methods and systems to parse a software component search query to enable multi entity search |
US11947530B2 (en) | 2021-02-24 | 2024-04-02 | Open Weaver Inc. | Methods and systems to automatically generate search queries from software documents to validate software component search engines |
US11954157B2 (en) | 2021-07-23 | 2024-04-09 | Veveo, Inc. | Method of and system for conducting personalized federated search and presentation of results therefrom |
Families Citing this family (409)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6193518B1 (en) * | 1998-11-20 | 2001-02-27 | Tina M. Nocera | Method for developing answer-options to issue-questions relating to child-development |
US6711585B1 (en) * | 1999-06-15 | 2004-03-23 | Kanisa Inc. | System and method for implementing a knowledge management system |
US7392185B2 (en) * | 1999-11-12 | 2008-06-24 | Phoenix Solutions, Inc. | Speech based learning/training system using semantic decoding |
EP1275042A2 (en) * | 2000-03-06 | 2003-01-15 | Kanisa Inc. | A system and method for providing an intelligent multi-step dialog with a user |
US8645137B2 (en) | 2000-03-16 | 2014-02-04 | Apple Inc. | Fast, language-independent method for user authentication by voice |
US7177798B2 (en) * | 2000-04-07 | 2007-02-13 | Rensselaer Polytechnic Institute | Natural language interface using constrained intermediate dictionary of results |
US6711561B1 (en) | 2000-05-02 | 2004-03-23 | Iphrase.Com, Inc. | Prose feedback in information access system |
US6704728B1 (en) * | 2000-05-02 | 2004-03-09 | Iphase.Com, Inc. | Accessing information from a collection of data |
US8478732B1 (en) | 2000-05-02 | 2013-07-02 | International Business Machines Corporation | Database aliasing in information access system |
US6408277B1 (en) | 2000-06-21 | 2002-06-18 | Banter Limited | System and method for automatic task prioritization |
US8290768B1 (en) | 2000-06-21 | 2012-10-16 | International Business Machines Corporation | System and method for determining a set of attributes based on content of communications |
US9699129B1 (en) | 2000-06-21 | 2017-07-04 | International Business Machines Corporation | System and method for increasing email productivity |
US7464086B2 (en) * | 2000-08-01 | 2008-12-09 | Yahoo! Inc. | Metatag-based datamining |
US20020032735A1 (en) * | 2000-08-25 | 2002-03-14 | Daniel Burnstein | Apparatus, means and methods for automatic community formation for phones and computer networks |
US20020152202A1 (en) * | 2000-08-30 | 2002-10-17 | Perro David J. | Method and system for retrieving information using natural language queries |
JP2002091971A (ja) * | 2000-09-11 | 2002-03-29 | Sony Corp | エージェントシステム、情報提供方法及び情報提供装置並びにデータ記録媒体 |
US7401125B1 (en) * | 2000-10-10 | 2008-07-15 | Ricoh Corporation | System, computer program product and method for managing documents |
US8340955B2 (en) | 2000-11-15 | 2012-12-25 | International Business Machines Corporation | System and method for finding the most likely answer to a natural language question |
AUPR208000A0 (en) * | 2000-12-15 | 2001-01-11 | 80-20 Software Pty Limited | Method of document searching |
US7644057B2 (en) | 2001-01-03 | 2010-01-05 | International Business Machines Corporation | System and method for electronic communication management |
US6714939B2 (en) * | 2001-01-08 | 2004-03-30 | Softface, Inc. | Creation of structured data from plain text |
US7027987B1 (en) * | 2001-02-07 | 2006-04-11 | Google Inc. | Voice interface for a search engine |
US7231381B2 (en) * | 2001-03-13 | 2007-06-12 | Microsoft Corporation | Media content search engine incorporating text content and user log mining |
US20020138315A1 (en) * | 2001-03-20 | 2002-09-26 | Mineo Nozaki | Technical support system |
JP2002278977A (ja) * | 2001-03-22 | 2002-09-27 | Fujitsu Ltd | 質問回答装置、質問回答方法及び質問回答プログラム |
US20020144293A1 (en) * | 2001-03-27 | 2002-10-03 | Koninklijke Philips Electronics N.V. | Automatic video retriever genie |
US6748398B2 (en) | 2001-03-30 | 2004-06-08 | Microsoft Corporation | Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR) |
US20020188498A1 (en) * | 2001-06-11 | 2002-12-12 | David Stoloff | System and method for soliciting customer feedback |
US7376620B2 (en) * | 2001-07-23 | 2008-05-20 | Consona Crm Inc. | System and method for measuring the quality of information retrieval |
ITFI20010199A1 (it) | 2001-10-22 | 2003-04-22 | Riccardo Vieri | Sistema e metodo per trasformare in voce comunicazioni testuali ed inviarle con una connessione internet a qualsiasi apparato telefonico |
US20030088453A1 (en) * | 2001-11-02 | 2003-05-08 | Toshiba Tec Kabushiki Kaisha | System, method and computer program product for rank assignment |
US20030088454A1 (en) * | 2001-11-02 | 2003-05-08 | Toshiba Tec Kabushiki Kaisha | System, method and computer program product for complaint report issue |
US20030088451A1 (en) * | 2001-11-02 | 2003-05-08 | Toshiba Tec Kabushiki Kaisha | Technical support system |
US20030088641A1 (en) * | 2001-11-02 | 2003-05-08 | Toshiba Tec Kabushiki Kaisha | Technical support system |
US20030115087A1 (en) * | 2001-11-02 | 2003-06-19 | Toshiba Tec Kabushiki Kaisha | Technical support system |
US7024411B2 (en) * | 2001-11-02 | 2006-04-04 | Toshiba Tec Kabushiki Kaisha | Method, system and computer program product for providing backup data for use in studying claims |
US20030088330A1 (en) * | 2001-11-02 | 2003-05-08 | Toshiba Tec Kabushiki Kaisha | Method, system and computer program product for coping with the changes in hardware |
US7283992B2 (en) * | 2001-11-30 | 2007-10-16 | Microsoft Corporation | Media agent to suggest contextually related media content |
US20030115191A1 (en) * | 2001-12-17 | 2003-06-19 | Max Copperman | Efficient and cost-effective content provider for customer relationship management (CRM) or other applications |
US7343372B2 (en) * | 2002-02-22 | 2008-03-11 | International Business Machines Corporation | Direct navigation for information retrieval |
US7737134B2 (en) * | 2002-03-13 | 2010-06-15 | The Texas A & M University System | Anticancer agents and use |
US7869998B1 (en) * | 2002-04-23 | 2011-01-11 | At&T Intellectual Property Ii, L.P. | Voice-enabled dialog system |
US7437349B2 (en) * | 2002-05-10 | 2008-10-14 | International Business Machines Corporation | Adaptive probabilistic query expansion |
JP2004139553A (ja) * | 2002-08-19 | 2004-05-13 | Matsushita Electric Ind Co Ltd | 文書検索システムおよび質問応答システム |
US20040098409A1 (en) * | 2002-11-15 | 2004-05-20 | Kelleher Kelly J. | Adaptable screening system and method |
US8645122B1 (en) | 2002-12-19 | 2014-02-04 | At&T Intellectual Property Ii, L.P. | Method of handling frequently asked questions in a natural language dialog service |
US7822612B1 (en) * | 2003-01-03 | 2010-10-26 | Verizon Laboratories Inc. | Methods of processing a voice command from a caller |
EP1443427A1 (en) * | 2003-01-29 | 2004-08-04 | Hewlett-Packard Company, A Delaware Corporation | Maintenance of information retrieval systems using global metrics |
US8495002B2 (en) | 2003-05-06 | 2013-07-23 | International Business Machines Corporation | Software tool for training and testing a knowledge base |
US20050187913A1 (en) | 2003-05-06 | 2005-08-25 | Yoram Nelken | Web-based customer service interface |
US7730175B1 (en) | 2003-05-12 | 2010-06-01 | Sourcefire, Inc. | Systems and methods for identifying the services of a network |
US20040254957A1 (en) * | 2003-06-13 | 2004-12-16 | Nokia Corporation | Method and a system for modeling user preferences |
US7409336B2 (en) * | 2003-06-19 | 2008-08-05 | Siebel Systems, Inc. | Method and system for searching data based on identified subset of categories and relevance-scored text representation-category combinations |
US7599938B1 (en) | 2003-07-11 | 2009-10-06 | Harrison Jr Shelton E | Social news gathering, prioritizing, tagging, searching, and syndication method |
US20070136251A1 (en) * | 2003-08-21 | 2007-06-14 | Idilia Inc. | System and Method for Processing a Query |
US7895221B2 (en) * | 2003-08-21 | 2011-02-22 | Idilia Inc. | Internet searching using semantic disambiguation and expansion |
JP2005092271A (ja) * | 2003-09-12 | 2005-04-07 | Hitachi Ltd | 質問応答方法及び質問応答装置 |
US7406662B2 (en) * | 2003-11-10 | 2008-07-29 | Microsoft Corporation | Data input panel character conversion |
JP2005173953A (ja) * | 2003-12-11 | 2005-06-30 | Matsushita Electric Ind Co Ltd | Faq検索システム |
US7356475B2 (en) | 2004-01-05 | 2008-04-08 | Sbc Knowledge Ventures, L.P. | System and method for providing access to an interactive service offering |
US20050165745A1 (en) * | 2004-01-13 | 2005-07-28 | International Business Machines Corporation | Method and apparatus for collecting user feedback based on search queries |
US20050177561A1 (en) * | 2004-02-06 | 2005-08-11 | Kumaresan Ramanathan | Learning search algorithm for indexing the web that converges to near perfect results for search queries |
US7831387B2 (en) * | 2004-03-23 | 2010-11-09 | Google Inc. | Visually-oriented driving directions in digital mapping system |
US8041713B2 (en) * | 2004-03-31 | 2011-10-18 | Google Inc. | Systems and methods for analyzing boilerplate |
US9009153B2 (en) | 2004-03-31 | 2015-04-14 | Google Inc. | Systems and methods for identifying a named entity |
US7693825B2 (en) | 2004-03-31 | 2010-04-06 | Google Inc. | Systems and methods for ranking implicit search results |
US20080040315A1 (en) * | 2004-03-31 | 2008-02-14 | Auerbach David B | Systems and methods for generating a user interface |
US7707142B1 (en) | 2004-03-31 | 2010-04-27 | Google Inc. | Methods and systems for performing an offline search |
US8631001B2 (en) * | 2004-03-31 | 2014-01-14 | Google Inc. | Systems and methods for weighting a search query result |
US7664734B2 (en) * | 2004-03-31 | 2010-02-16 | Google Inc. | Systems and methods for generating multiple implicit search queries |
US7272601B1 (en) * | 2004-03-31 | 2007-09-18 | Google Inc. | Systems and methods for associating a keyword with a user interface area |
US7792954B2 (en) * | 2004-04-02 | 2010-09-07 | Webtrends, Inc. | Systems and methods for tracking web activity |
US7747601B2 (en) * | 2006-08-14 | 2010-06-29 | Inquira, Inc. | Method and apparatus for identifying and classifying query intent |
US8082264B2 (en) | 2004-04-07 | 2011-12-20 | Inquira, Inc. | Automated scheme for identifying user intent in real-time |
US8612208B2 (en) | 2004-04-07 | 2013-12-17 | Oracle Otc Subsidiary Llc | Ontology for use with a system, method, and computer readable medium for retrieving information and response to a query |
US7310635B2 (en) * | 2004-05-17 | 2007-12-18 | Knowitall, Llc. | Record management and retrieval computer program and method |
US7254576B1 (en) | 2004-05-17 | 2007-08-07 | Microsoft Corporation | System and method for locating and presenting electronic documents to a user |
US7552124B2 (en) * | 2004-06-17 | 2009-06-23 | Ixi Mobile (R&D), Ltd. | Natural language for programming a specialized computing system |
WO2006007194A1 (en) * | 2004-06-25 | 2006-01-19 | Personasearch, Inc. | Dynamic search processor |
US7720674B2 (en) * | 2004-06-29 | 2010-05-18 | Sap Ag | Systems and methods for processing natural language queries |
US7725463B2 (en) * | 2004-06-30 | 2010-05-25 | Microsoft Corporation | System and method for generating normalized relevance measure for analysis of search results |
US8131754B1 (en) | 2004-06-30 | 2012-03-06 | Google Inc. | Systems and methods for determining an article association measure |
US7617176B2 (en) * | 2004-07-13 | 2009-11-10 | Microsoft Corporation | Query-based snippet clustering for search result grouping |
US7539681B2 (en) * | 2004-07-26 | 2009-05-26 | Sourcefire, Inc. | Methods and systems for multi-pattern searching |
US7602898B2 (en) | 2004-08-18 | 2009-10-13 | At&T Intellectual Property I, L.P. | System and method for providing computer assisted user support |
US20060062470A1 (en) * | 2004-09-22 | 2006-03-23 | Microsoft Corporation | Graphical user interface for expression recognition |
US7929767B2 (en) * | 2004-09-22 | 2011-04-19 | Microsoft Corporation | Analyzing subordinate sub-expressions in expression recognition |
US7561739B2 (en) * | 2004-09-22 | 2009-07-14 | Microsoft Corporation | Analyzing scripts and determining characters in expression recognition |
US7561737B2 (en) * | 2004-09-22 | 2009-07-14 | Microsoft Corporation | Mathematical expression recognition |
US7561738B2 (en) * | 2004-09-22 | 2009-07-14 | Microsoft Corporation | Symbol grouping and recognition in expression recognition |
US7447360B2 (en) * | 2004-09-22 | 2008-11-04 | Microsoft Corporation | Analyzing tabular structures in expression recognition |
US20060062375A1 (en) * | 2004-09-23 | 2006-03-23 | Sbc Knowledge Ventures, L.P. | System and method for providing product offers at a call center |
US7996208B2 (en) | 2004-09-30 | 2011-08-09 | Google Inc. | Methods and systems for selecting a language for text segmentation |
US7680648B2 (en) * | 2004-09-30 | 2010-03-16 | Google Inc. | Methods and systems for improving text segmentation |
US20060085392A1 (en) * | 2004-09-30 | 2006-04-20 | Microsoft Corporation | System and method for automatic generation of search results based on local intention |
US8051096B1 (en) | 2004-09-30 | 2011-11-01 | Google Inc. | Methods and systems for augmenting a token lexicon |
US7925506B2 (en) * | 2004-10-05 | 2011-04-12 | Inago Corporation | Speech recognition accuracy via concept to keyword mapping |
ATE470218T1 (de) * | 2004-10-05 | 2010-06-15 | Inago Corp | System und verfahren zur verbesserung der genauigkeit der spracherkennung |
US9330175B2 (en) | 2004-11-12 | 2016-05-03 | Make Sence, Inc. | Techniques for knowledge discovery by constructing knowledge correlations using concepts or terms |
US8108389B2 (en) | 2004-11-12 | 2012-01-31 | Make Sence, Inc. | Techniques for knowledge discovery by constructing knowledge correlations using concepts or terms |
US8126890B2 (en) * | 2004-12-21 | 2012-02-28 | Make Sence, Inc. | Techniques for knowledge discovery by constructing knowledge correlations using concepts or terms |
WO2006062868A2 (en) * | 2004-12-06 | 2006-06-15 | Yahoo! Inc. | Systems and methods for managing and using multiple concept networks for assisted search processing |
US20060126808A1 (en) * | 2004-12-13 | 2006-06-15 | Sbc Knowledge Ventures, L.P. | System and method for measurement of call deflection |
US20060136403A1 (en) * | 2004-12-22 | 2006-06-22 | Koo Charles C | System and method for digital content searching based on determined intent |
US8843536B1 (en) | 2004-12-31 | 2014-09-23 | Google Inc. | Methods and systems for providing relevant advertisements or other content for inactive uniform resource locators using search queries |
US20060161537A1 (en) * | 2005-01-19 | 2006-07-20 | International Business Machines Corporation | Detecting content-rich text |
WO2006083684A2 (en) * | 2005-01-28 | 2006-08-10 | Aol Llc | Web query classification |
JP2008529173A (ja) * | 2005-01-31 | 2008-07-31 | テキストディガー,インコーポレイテッド | 電子文書の意味検索および取り込みのための方法およびシステム |
US20060188087A1 (en) * | 2005-02-18 | 2006-08-24 | Sbc Knowledge Ventures, Lp | System and method for caller-controlled music on-hold |
US7461059B2 (en) * | 2005-02-23 | 2008-12-02 | Microsoft Corporation | Dynamically updated search results based upon continuously-evolving search query that is based at least in part upon phrase suggestion, search engine uses previous result sets performing additional search tasks |
US9400838B2 (en) | 2005-04-11 | 2016-07-26 | Textdigger, Inc. | System and method for searching for a query |
US20060245654A1 (en) * | 2005-04-29 | 2006-11-02 | Microsoft Corporation | Utilizing grammatical parsing for structured layout analysis |
US20060265232A1 (en) * | 2005-05-20 | 2006-11-23 | Microsoft Corporation | Adaptive customer assistance system for software products |
US7627564B2 (en) * | 2005-06-21 | 2009-12-01 | Microsoft Corporation | High scale adaptive search systems and methods |
US8140559B2 (en) * | 2005-06-27 | 2012-03-20 | Make Sence, Inc. | Knowledge correlation search engine |
US8898134B2 (en) | 2005-06-27 | 2014-11-25 | Make Sence, Inc. | Method for ranking resources using node pool |
US8249344B2 (en) * | 2005-07-01 | 2012-08-21 | Microsoft Corporation | Grammatical parsing of document visual structures |
US7809551B2 (en) * | 2005-07-01 | 2010-10-05 | Xerox Corporation | Concept matching system |
US20070038623A1 (en) * | 2005-08-10 | 2007-02-15 | Francois Huet | Method and apparatus to provide answers to a search engine natural language query |
WO2007026392A1 (ja) * | 2005-08-30 | 2007-03-08 | Spansion Llc | 半導体装置およびその製造方法 |
US8265939B2 (en) * | 2005-08-31 | 2012-09-11 | Nuance Communications, Inc. | Hierarchical methods and apparatus for extracting user intent from spoken utterances |
US8677377B2 (en) | 2005-09-08 | 2014-03-18 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US8024653B2 (en) | 2005-11-14 | 2011-09-20 | Make Sence, Inc. | Techniques for creating computer generated notes |
US8046833B2 (en) | 2005-11-14 | 2011-10-25 | Sourcefire, Inc. | Intrusion event correlation with network discovery information |
US7733803B2 (en) * | 2005-11-14 | 2010-06-08 | Sourcefire, Inc. | Systems and methods for modifying network map attributes |
US8694530B2 (en) | 2006-01-03 | 2014-04-08 | Textdigger, Inc. | Search system with query refinement and search method |
US7644373B2 (en) | 2006-01-23 | 2010-01-05 | Microsoft Corporation | User interface for viewing clusters of images |
US7836050B2 (en) * | 2006-01-25 | 2010-11-16 | Microsoft Corporation | Ranking content based on relevance and quality |
US8874591B2 (en) * | 2006-01-31 | 2014-10-28 | Microsoft Corporation | Using user feedback to improve search results |
US7814040B1 (en) | 2006-01-31 | 2010-10-12 | The Research Foundation Of State University Of New York | System and method for image annotation and multi-modal image retrieval using probabilistic semantic models |
US8509563B2 (en) * | 2006-02-02 | 2013-08-13 | Microsoft Corporation | Generation of documents from images |
US8719005B1 (en) * | 2006-02-10 | 2014-05-06 | Rusty Shawn Lee | Method and apparatus for using directed reasoning to respond to natural language queries |
JP5169816B2 (ja) * | 2006-03-01 | 2013-03-27 | 日本電気株式会社 | 質問回答装置、質問回答方法および質問回答用プログラム |
US7599861B2 (en) | 2006-03-02 | 2009-10-06 | Convergys Customer Management Group, Inc. | System and method for closed loop decisionmaking in an automated care system |
WO2007114932A2 (en) | 2006-04-04 | 2007-10-11 | Textdigger, Inc. | Search system and method with text function tagging |
US8126874B2 (en) * | 2006-05-09 | 2012-02-28 | Google Inc. | Systems and methods for generating statistics from search engine query logs |
US7921099B2 (en) | 2006-05-10 | 2011-04-05 | Inquira, Inc. | Guided navigation system |
US8379830B1 (en) | 2006-05-22 | 2013-02-19 | Convergys Customer Management Delaware Llc | System and method for automated customer service with contingent live interaction |
US7809663B1 (en) | 2006-05-22 | 2010-10-05 | Convergys Cmg Utah, Inc. | System and method for supporting the utilization of machine language |
US20070282816A1 (en) * | 2006-06-05 | 2007-12-06 | Shing-Jung Tsai | Method and structure for string partial search |
US7716236B2 (en) * | 2006-07-06 | 2010-05-11 | Aol Inc. | Temporal search query personalization |
US7792967B2 (en) * | 2006-07-14 | 2010-09-07 | Chacha Search, Inc. | Method and system for sharing and accessing resources |
US8255383B2 (en) * | 2006-07-14 | 2012-08-28 | Chacha Search, Inc | Method and system for qualifying keywords in query strings |
WO2008012834A2 (en) * | 2006-07-25 | 2008-01-31 | Jain Pankaj | A method and a system for searching information using information device |
US7948988B2 (en) * | 2006-07-27 | 2011-05-24 | Sourcefire, Inc. | Device, system and method for analysis of fragments in a fragment train |
US8676868B2 (en) * | 2006-08-04 | 2014-03-18 | Chacha Search, Inc | Macro programming for resources |
US8024308B2 (en) * | 2006-08-07 | 2011-09-20 | Chacha Search, Inc | Electronic previous search results log |
US7701945B2 (en) * | 2006-08-10 | 2010-04-20 | Sourcefire, Inc. | Device, system and method for analysis of segments in a transmission control protocol (TCP) session |
US8781813B2 (en) * | 2006-08-14 | 2014-07-15 | Oracle Otc Subsidiary Llc | Intent management tool for identifying concepts associated with a plurality of users' queries |
US9318108B2 (en) | 2010-01-18 | 2016-04-19 | Apple Inc. | Intelligent automated assistant |
US7657504B2 (en) * | 2006-10-10 | 2010-02-02 | Microsoft Corporation | User interface for displaying images of sights |
US7707208B2 (en) * | 2006-10-10 | 2010-04-27 | Microsoft Corporation | Identifying sight for a location |
US7698259B2 (en) * | 2006-11-22 | 2010-04-13 | Sap Ag | Semantic search in a database |
US8095476B2 (en) * | 2006-11-27 | 2012-01-10 | Inquira, Inc. | Automated support scheme for electronic forms |
US7650317B2 (en) * | 2006-12-06 | 2010-01-19 | Microsoft Corporation | Active learning framework for automatic field extraction from network traffic |
US8661012B1 (en) * | 2006-12-29 | 2014-02-25 | Google Inc. | Ensuring that a synonym for a query phrase does not drop information present in the query phrase |
US8069352B2 (en) * | 2007-02-28 | 2011-11-29 | Sourcefire, Inc. | Device, system and method for timestamp analysis of segments in a transmission control protocol (TCP) session |
US8977255B2 (en) | 2007-04-03 | 2015-03-10 | Apple Inc. | Method and system for operating a multi-function portable electronic device using voice-activation |
CN105589571A (zh) * | 2007-04-09 | 2016-05-18 | 谷歌股份有限公司 | 客户端输入方法以及输入法编辑器服务器 |
WO2008134057A1 (en) * | 2007-04-30 | 2008-11-06 | Sourcefire, Inc. | Real-time awareness for a computer network |
US8478515B1 (en) * | 2007-05-23 | 2013-07-02 | Google Inc. | Collaborative driving directions |
US20080319975A1 (en) * | 2007-06-22 | 2008-12-25 | Microsoft Corporation | Exploratory Search Technique |
US20090006358A1 (en) * | 2007-06-27 | 2009-01-01 | Microsoft Corporation | Search results |
US8260619B1 (en) | 2008-08-22 | 2012-09-04 | Convergys Cmg Utah, Inc. | Method and system for creating natural language understanding grammars |
US8316036B2 (en) | 2007-08-31 | 2012-11-20 | Microsoft Corporation | Checkpointing iterators during search |
US8463593B2 (en) | 2007-08-31 | 2013-06-11 | Microsoft Corporation | Natural language hypernym weighting for word sense disambiguation |
US8712758B2 (en) * | 2007-08-31 | 2014-04-29 | Microsoft Corporation | Coreference resolution in an ambiguity-sensitive natural language processing system |
US8280721B2 (en) * | 2007-08-31 | 2012-10-02 | Microsoft Corporation | Efficiently representing word sense probabilities |
US8229970B2 (en) * | 2007-08-31 | 2012-07-24 | Microsoft Corporation | Efficient storage and retrieval of posting lists |
US7984032B2 (en) * | 2007-08-31 | 2011-07-19 | Microsoft Corporation | Iterators for applying term occurrence-level constraints in natural language searching |
US8041697B2 (en) * | 2007-08-31 | 2011-10-18 | Microsoft Corporation | Semi-automatic example-based induction of semantic translation rules to support natural language search |
US8346756B2 (en) * | 2007-08-31 | 2013-01-01 | Microsoft Corporation | Calculating valence of expressions within documents for searching a document index |
US9053089B2 (en) | 2007-10-02 | 2015-06-09 | Apple Inc. | Part-of-speech tagging using latent analogy |
US9330720B2 (en) | 2008-01-03 | 2016-05-03 | Apple Inc. | Methods and apparatus for altering audio output signals |
WO2009094633A1 (en) | 2008-01-25 | 2009-07-30 | Chacha Search, Inc. | Method and system for access to restricted resource(s) |
US8065143B2 (en) | 2008-02-22 | 2011-11-22 | Apple Inc. | Providing text input using speech data and non-speech data |
US20090234803A1 (en) * | 2008-03-11 | 2009-09-17 | Continental Electrical Construction Company, Llc | Keyword search of business information system |
US8996376B2 (en) | 2008-04-05 | 2015-03-31 | Apple Inc. | Intelligent text-to-speech conversion |
US8474043B2 (en) | 2008-04-17 | 2013-06-25 | Sourcefire, Inc. | Speed and memory optimization of intrusion detection system (IDS) and intrusion prevention system (IPS) rule processing |
US8719256B2 (en) * | 2008-05-01 | 2014-05-06 | Chacha Search, Inc | Method and system for improvement of request processing |
US10496753B2 (en) | 2010-01-18 | 2019-12-03 | Apple Inc. | Automatically adapting user interfaces for hands-free interaction |
CN101286161B (zh) * | 2008-05-28 | 2010-10-06 | 华中科技大学 | 一种基于概念的智能中文问答系统 |
US20090307203A1 (en) * | 2008-06-04 | 2009-12-10 | Gregory Keim | Method of locating content for language learning |
US8464150B2 (en) | 2008-06-07 | 2013-06-11 | Apple Inc. | Automatic language identification for dynamic text processing |
US20090313286A1 (en) * | 2008-06-17 | 2009-12-17 | Microsoft Corporation | Generating training data from click logs |
US20100011282A1 (en) * | 2008-07-11 | 2010-01-14 | iCyte Pty Ltd. | Annotation system and method |
US20100030549A1 (en) | 2008-07-31 | 2010-02-04 | Lee Michael M | Mobile device having human language translation capability with positional feedback |
US8112269B2 (en) * | 2008-08-25 | 2012-02-07 | Microsoft Corporation | Determining utility of a question |
US8768702B2 (en) | 2008-09-05 | 2014-07-01 | Apple Inc. | Multi-tiered voice feedback in an electronic device |
US8898568B2 (en) | 2008-09-09 | 2014-11-25 | Apple Inc. | Audio user interface |
US8712776B2 (en) | 2008-09-29 | 2014-04-29 | Apple Inc. | Systems and methods for selective text to speech synthesis |
US8676904B2 (en) | 2008-10-02 | 2014-03-18 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US8272055B2 (en) | 2008-10-08 | 2012-09-18 | Sourcefire, Inc. | Target-based SMB and DCE/RPC processing for an intrusion detection system or intrusion prevention system |
US9959870B2 (en) | 2008-12-11 | 2018-05-01 | Apple Inc. | Speech recognition involving a mobile device |
US7669147B1 (en) | 2009-01-02 | 2010-02-23 | International Business Machines Corporation | Reorienting navigation trees based on semantic grouping of repeating tree nodes |
US8862252B2 (en) | 2009-01-30 | 2014-10-14 | Apple Inc. | Audio user interface for displayless electronic device |
US8380507B2 (en) | 2009-03-09 | 2013-02-19 | Apple Inc. | Systems and methods for determining the language to use for speech generated by a text to speech engine |
FR2943159B1 (fr) * | 2009-03-16 | 2016-10-21 | Alcatel Lucent | Procede d'assistance a un operateur d'un centre d'appels |
US9336299B2 (en) | 2009-04-20 | 2016-05-10 | Microsoft Technology Licensing, Llc | Acquisition of semantic class lexicons for query tagging |
US8949241B2 (en) | 2009-05-08 | 2015-02-03 | Thomson Reuters Global Resources | Systems and methods for interactive disambiguation of data |
US9858925B2 (en) | 2009-06-05 | 2018-01-02 | Apple Inc. | Using context information to facilitate processing of commands in a virtual assistant |
US10540976B2 (en) | 2009-06-05 | 2020-01-21 | Apple Inc. | Contextual voice commands |
US10241644B2 (en) | 2011-06-03 | 2019-03-26 | Apple Inc. | Actionable reminder entries |
US10255566B2 (en) | 2011-06-03 | 2019-04-09 | Apple Inc. | Generating and processing task items that represent tasks to perform |
US10241752B2 (en) | 2011-09-30 | 2019-03-26 | Apple Inc. | Interface for a virtual digital assistant |
US20100332493A1 (en) * | 2009-06-25 | 2010-12-30 | Yahoo! Inc. | Semantic search extensions for web search engines |
US9431006B2 (en) | 2009-07-02 | 2016-08-30 | Apple Inc. | Methods and apparatuses for automatic speech recognition |
CN102012900B (zh) * | 2009-09-04 | 2013-01-30 | 阿里巴巴集团控股有限公司 | 信息检索方法和系统 |
US8682649B2 (en) | 2009-11-12 | 2014-03-25 | Apple Inc. | Sentiment prediction from textual data |
US8311838B2 (en) | 2010-01-13 | 2012-11-13 | Apple Inc. | Devices and methods for identifying a prompt corresponding to a voice input in a sequence of prompts |
US8381107B2 (en) | 2010-01-13 | 2013-02-19 | Apple Inc. | Adaptive audio feedback system and method |
US10705794B2 (en) | 2010-01-18 | 2020-07-07 | Apple Inc. | Automatically adapting user interfaces for hands-free interaction |
US10276170B2 (en) | 2010-01-18 | 2019-04-30 | Apple Inc. | Intelligent automated assistant |
US10553209B2 (en) | 2010-01-18 | 2020-02-04 | Apple Inc. | Systems and methods for hands-free notification summaries |
US10679605B2 (en) | 2010-01-18 | 2020-06-09 | Apple Inc. | Hands-free list-reading by intelligent automated assistant |
DE202011111062U1 (de) | 2010-01-25 | 2019-02-19 | Newvaluexchange Ltd. | Vorrichtung und System für eine Digitalkonversationsmanagementplattform |
US8682667B2 (en) | 2010-02-25 | 2014-03-25 | Apple Inc. | User profiling for selecting user specific voice input processing information |
EP2559217B1 (en) | 2010-04-16 | 2019-08-14 | Cisco Technology, Inc. | System and method for near-real time network attack detection, and system and method for unified detection via detection routing |
US8161073B2 (en) | 2010-05-05 | 2012-04-17 | Holovisions, LLC | Context-driven search |
US20110289070A1 (en) * | 2010-05-20 | 2011-11-24 | Lockheed Martin Corporation | Dynamic resource orchestration system for data retrieval and output generation |
US8849807B2 (en) | 2010-05-25 | 2014-09-30 | Mark F. McLellan | Active search results page ranking technology |
US8433790B2 (en) | 2010-06-11 | 2013-04-30 | Sourcefire, Inc. | System and method for assigning network blocks to sensors |
US8671182B2 (en) | 2010-06-22 | 2014-03-11 | Sourcefire, Inc. | System and method for resolving operating system or service identity conflicts |
US8713021B2 (en) | 2010-07-07 | 2014-04-29 | Apple Inc. | Unsupervised document clustering using latent semantic density analysis |
US8719006B2 (en) | 2010-08-27 | 2014-05-06 | Apple Inc. | Combined statistical and rule-based part-of-speech tagging for text-to-speech synthesis |
US8719014B2 (en) | 2010-09-27 | 2014-05-06 | Apple Inc. | Electronic device with text error correction based on voice recognition data |
EP2622592A4 (en) * | 2010-09-28 | 2017-04-05 | International Business Machines Corporation | Providing answers to questions using multiple models to score candidate answers |
US9928296B2 (en) | 2010-12-16 | 2018-03-27 | Microsoft Technology Licensing, Llc | Search lexicon expansion |
US10515147B2 (en) | 2010-12-22 | 2019-12-24 | Apple Inc. | Using statistical language models for contextual lookup |
US10762293B2 (en) | 2010-12-22 | 2020-09-01 | Apple Inc. | Using parts-of-speech tagging and named entity recognition for spelling correction |
US8781836B2 (en) | 2011-02-22 | 2014-07-15 | Apple Inc. | Hearing assistance system for providing consistent human speech |
US8688453B1 (en) * | 2011-02-28 | 2014-04-01 | Nuance Communications, Inc. | Intent mining via analysis of utterances |
US8601034B2 (en) | 2011-03-11 | 2013-12-03 | Sourcefire, Inc. | System and method for real time data awareness |
US9262612B2 (en) | 2011-03-21 | 2016-02-16 | Apple Inc. | Device access using voice authentication |
US10057736B2 (en) | 2011-06-03 | 2018-08-21 | Apple Inc. | Active transport based notifications |
US10672399B2 (en) | 2011-06-03 | 2020-06-02 | Apple Inc. | Switching between text data and audio data based on a mapping |
US8812294B2 (en) | 2011-06-21 | 2014-08-19 | Apple Inc. | Translating phrases from one language into another using an order-based set of declarative rules |
US8706472B2 (en) | 2011-08-11 | 2014-04-22 | Apple Inc. | Method for disambiguating multiple readings in language conversion |
US8994660B2 (en) | 2011-08-29 | 2015-03-31 | Apple Inc. | Text correction processing |
US10387536B2 (en) | 2011-09-19 | 2019-08-20 | Personetics Technologies Ltd. | Computerized data-aware agent systems for retrieving data to serve a dialog between human user and computerized system |
US11068532B2 (en) | 2011-09-21 | 2021-07-20 | Horsetooth Ventures, LLC | Interactive image display and selection system |
US9734167B2 (en) | 2011-09-21 | 2017-08-15 | Horsetooth Ventures, LLC | Interactive image display and selection system |
US8762156B2 (en) | 2011-09-28 | 2014-06-24 | Apple Inc. | Speech recognition repair using contextual information |
US9117194B2 (en) | 2011-12-06 | 2015-08-25 | Nuance Communications, Inc. | Method and apparatus for operating a frequently asked questions (FAQ)-based system |
US9720930B2 (en) * | 2012-01-30 | 2017-08-01 | Accenture Global Services Limited | Travel management |
US10134385B2 (en) | 2012-03-02 | 2018-11-20 | Apple Inc. | Systems and methods for name pronunciation |
US20130232022A1 (en) * | 2012-03-05 | 2013-09-05 | Hermann Geupel | System and method for rating online offered information |
US9483461B2 (en) | 2012-03-06 | 2016-11-01 | Apple Inc. | Handling speech synthesis of content for multiple languages |
US9280610B2 (en) | 2012-05-14 | 2016-03-08 | Apple Inc. | Crowd sourcing information to fulfill user requests |
US8775442B2 (en) | 2012-05-15 | 2014-07-08 | Apple Inc. | Semantic search using a single-source semantic model |
US10417037B2 (en) | 2012-05-15 | 2019-09-17 | Apple Inc. | Systems and methods for integrating third party services with a digital assistant |
US9721563B2 (en) | 2012-06-08 | 2017-08-01 | Apple Inc. | Name recognition system |
WO2013185109A2 (en) | 2012-06-08 | 2013-12-12 | Apple Inc. | Systems and methods for recognizing textual identifiers within a plurality of words |
US9495129B2 (en) | 2012-06-29 | 2016-11-15 | Apple Inc. | Device, method, and user interface for voice-activated navigation and browsing of a document |
US9576574B2 (en) | 2012-09-10 | 2017-02-21 | Apple Inc. | Context-sensitive handling of interruptions by intelligent digital assistant |
US9547647B2 (en) | 2012-09-19 | 2017-01-17 | Apple Inc. | Voice-based media searching |
US8935167B2 (en) | 2012-09-25 | 2015-01-13 | Apple Inc. | Exemplar-based latent perceptual modeling for automatic speech recognition |
US9411803B2 (en) | 2012-09-28 | 2016-08-09 | Hewlett Packard Enterprise Development Lp | Responding to natural language queries |
JP6063217B2 (ja) * | 2012-11-16 | 2017-01-18 | 任天堂株式会社 | プログラム、情報処理装置、情報処理システム、および情報処理方法 |
US9015097B2 (en) | 2012-12-19 | 2015-04-21 | Nuance Communications, Inc. | System and method for learning answers to frequently asked questions from a semi-structured data source |
CN103914480B (zh) * | 2013-01-07 | 2017-07-14 | 重庆新媒农信科技有限公司 | 一种用于自动应答系统的数据查询方法、控制器及系统 |
US9069882B2 (en) * | 2013-01-22 | 2015-06-30 | International Business Machines Corporation | Mapping and boosting of terms in a format independent data retrieval query |
KR20230137475A (ko) | 2013-02-07 | 2023-10-04 | 애플 인크. | 디지털 어시스턴트를 위한 음성 트리거 |
US9292537B1 (en) | 2013-02-23 | 2016-03-22 | Bryant Christopher Lee | Autocompletion of filename based on text in a file to be saved |
US9733821B2 (en) | 2013-03-14 | 2017-08-15 | Apple Inc. | Voice control to diagnose inadvertent activation of accessibility features |
US10652394B2 (en) | 2013-03-14 | 2020-05-12 | Apple Inc. | System and method for processing voicemail |
US10642574B2 (en) | 2013-03-14 | 2020-05-05 | Apple Inc. | Device, method, and graphical user interface for outputting captions |
US9977779B2 (en) | 2013-03-14 | 2018-05-22 | Apple Inc. | Automatic supplementation of word correction dictionaries |
US9213748B1 (en) | 2013-03-14 | 2015-12-15 | Google Inc. | Generating related questions for search queries |
US9368114B2 (en) | 2013-03-14 | 2016-06-14 | Apple Inc. | Context-sensitive handling of interruptions |
US10572476B2 (en) | 2013-03-14 | 2020-02-25 | Apple Inc. | Refining a search based on schedule items |
KR101857648B1 (ko) | 2013-03-15 | 2018-05-15 | 애플 인크. | 지능형 디지털 어시스턴트에 의한 사용자 트레이닝 |
WO2014144579A1 (en) | 2013-03-15 | 2014-09-18 | Apple Inc. | System and method for updating an adaptive speech recognition model |
AU2014233517B2 (en) | 2013-03-15 | 2017-05-25 | Apple Inc. | Training an at least partial voice command system |
AU2014251347B2 (en) | 2013-03-15 | 2017-05-18 | Apple Inc. | Context-sensitive handling of interruptions |
US10748529B1 (en) | 2013-03-15 | 2020-08-18 | Apple Inc. | Voice activated device for use with a voice-based digital assistant |
US9064001B2 (en) | 2013-03-15 | 2015-06-23 | Nuance Communications, Inc. | Method and apparatus for a frequently-asked questions portal workflow |
US8965915B2 (en) | 2013-03-17 | 2015-02-24 | Alation, Inc. | Assisted query formation, validation, and result previewing in a database having a complex schema |
US9373322B2 (en) * | 2013-04-10 | 2016-06-21 | Nuance Communications, Inc. | System and method for determining query intent |
US9317608B2 (en) | 2013-05-03 | 2016-04-19 | Mapquest, Inc. | Systems and methods for parsing search queries |
CN103268312B (zh) * | 2013-05-03 | 2016-04-06 | 同济大学 | 一种基于用户反馈的训练语料收集系统及其方法 |
WO2014197334A2 (en) | 2013-06-07 | 2014-12-11 | Apple Inc. | System and method for user-specified pronunciation of words for speech synthesis and recognition |
US9582608B2 (en) | 2013-06-07 | 2017-02-28 | Apple Inc. | Unified ranking with entropy-weighted information for phrase-based semantic auto-completion |
WO2014197336A1 (en) | 2013-06-07 | 2014-12-11 | Apple Inc. | System and method for detecting errors in interactions with a voice-based digital assistant |
WO2014197335A1 (en) | 2013-06-08 | 2014-12-11 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
US10176167B2 (en) | 2013-06-09 | 2019-01-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
EP3937002A1 (en) | 2013-06-09 | 2022-01-12 | Apple Inc. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
AU2014278595B2 (en) | 2013-06-13 | 2017-04-06 | Apple Inc. | System and method for emergency calls initiated by voice command |
DE112014003653B4 (de) | 2013-08-06 | 2024-04-18 | Apple Inc. | Automatisch aktivierende intelligente Antworten auf der Grundlage von Aktivitäten von entfernt angeordneten Vorrichtungen |
WO2015051397A1 (en) * | 2013-10-10 | 2015-04-16 | Quikser Pty Ltd | A server for serving answer data and a computer readable storage medium for serving answer data |
CN103577558B (zh) * | 2013-10-21 | 2017-04-26 | 北京奇虎科技有限公司 | 一种优化问答对的搜索排名的装置和方法 |
US10296160B2 (en) | 2013-12-06 | 2019-05-21 | Apple Inc. | Method for extracting salient dialog usage from live data |
US9558176B2 (en) | 2013-12-06 | 2017-01-31 | Microsoft Technology Licensing, Llc | Discriminating between natural language and keyword language items |
US20150301795A1 (en) * | 2014-04-16 | 2015-10-22 | Facebook, Inc. | Crowd sourced based training for natural language interface systems |
US9934306B2 (en) | 2014-05-12 | 2018-04-03 | Microsoft Technology Licensing, Llc | Identifying query intent |
US9620105B2 (en) | 2014-05-15 | 2017-04-11 | Apple Inc. | Analyzing audio input for efficient speech and music recognition |
CN103995870A (zh) * | 2014-05-21 | 2014-08-20 | 百度在线网络技术(北京)有限公司 | 交互式搜索方法和装置 |
US10592095B2 (en) | 2014-05-23 | 2020-03-17 | Apple Inc. | Instantaneous speaking of content on touch devices |
US9502031B2 (en) | 2014-05-27 | 2016-11-22 | Apple Inc. | Method for supporting dynamic grammars in WFST-based ASR |
US9734193B2 (en) | 2014-05-30 | 2017-08-15 | Apple Inc. | Determining domain salience ranking from ambiguous words in natural speech |
US9715875B2 (en) | 2014-05-30 | 2017-07-25 | Apple Inc. | Reducing the need for manual start/end-pointing and trigger phrases |
US10289433B2 (en) | 2014-05-30 | 2019-05-14 | Apple Inc. | Domain specific language for encoding assistant dialog |
US9430463B2 (en) | 2014-05-30 | 2016-08-30 | Apple Inc. | Exemplar-based natural language processing |
US9760559B2 (en) | 2014-05-30 | 2017-09-12 | Apple Inc. | Predictive text input |
AU2015266863B2 (en) | 2014-05-30 | 2018-03-15 | Apple Inc. | Multi-command single utterance input method |
US10170123B2 (en) | 2014-05-30 | 2019-01-01 | Apple Inc. | Intelligent assistant for home automation |
US9842101B2 (en) | 2014-05-30 | 2017-12-12 | Apple Inc. | Predictive conversion of language input |
US9785630B2 (en) | 2014-05-30 | 2017-10-10 | Apple Inc. | Text prediction using combined word N-gram and unigram language models |
US9633004B2 (en) | 2014-05-30 | 2017-04-25 | Apple Inc. | Better resolution when referencing to concepts |
US10078631B2 (en) | 2014-05-30 | 2018-09-18 | Apple Inc. | Entropy-guided text prediction using combined word and character n-gram language models |
US9338493B2 (en) | 2014-06-30 | 2016-05-10 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US10659851B2 (en) | 2014-06-30 | 2020-05-19 | Apple Inc. | Real-time digital assistant knowledge updates |
US9798801B2 (en) | 2014-07-16 | 2017-10-24 | Microsoft Technology Licensing, Llc | Observation-based query interpretation model modification |
US10446141B2 (en) | 2014-08-28 | 2019-10-15 | Apple Inc. | Automatic speech recognition based on user feedback |
US9818400B2 (en) | 2014-09-11 | 2017-11-14 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US10789041B2 (en) | 2014-09-12 | 2020-09-29 | Apple Inc. | Dynamic thresholds for always listening speech trigger |
US9646609B2 (en) | 2014-09-30 | 2017-05-09 | Apple Inc. | Caching apparatus for serving phonetic pronunciations |
US9668121B2 (en) | 2014-09-30 | 2017-05-30 | Apple Inc. | Social reminders |
US9886432B2 (en) | 2014-09-30 | 2018-02-06 | Apple Inc. | Parsimonious handling of word inflection via categorical stem + suffix N-gram language models |
US10127911B2 (en) | 2014-09-30 | 2018-11-13 | Apple Inc. | Speaker identification and unsupervised speaker adaptation techniques |
US10074360B2 (en) | 2014-09-30 | 2018-09-11 | Apple Inc. | Providing an indication of the suitability of speech recognition |
US10552013B2 (en) | 2014-12-02 | 2020-02-04 | Apple Inc. | Data detection |
US9711141B2 (en) | 2014-12-09 | 2017-07-18 | Apple Inc. | Disambiguating heteronyms in speech synthesis |
CN104573028B (zh) * | 2015-01-14 | 2019-01-25 | 百度在线网络技术(北京)有限公司 | 实现智能问答的方法和系统 |
US9767091B2 (en) | 2015-01-23 | 2017-09-19 | Microsoft Technology Licensing, Llc | Methods for understanding incomplete natural language query |
US9865280B2 (en) | 2015-03-06 | 2018-01-09 | Apple Inc. | Structured dictation using intelligent automated assistants |
US9886953B2 (en) | 2015-03-08 | 2018-02-06 | Apple Inc. | Virtual assistant activation |
US10567477B2 (en) | 2015-03-08 | 2020-02-18 | Apple Inc. | Virtual assistant continuity |
US9721566B2 (en) | 2015-03-08 | 2017-08-01 | Apple Inc. | Competing devices responding to voice triggers |
US9165057B1 (en) | 2015-03-10 | 2015-10-20 | Bank Of America Corporation | Method and apparatus for extracting queries from webpages |
US9899019B2 (en) | 2015-03-18 | 2018-02-20 | Apple Inc. | Systems and methods for structured stem and suffix language models |
US9842105B2 (en) | 2015-04-16 | 2017-12-12 | Apple Inc. | Parsimonious continuous-space phrase representations for natural language processing |
US10083688B2 (en) | 2015-05-27 | 2018-09-25 | Apple Inc. | Device voice control for selecting a displayed affordance |
US10102275B2 (en) | 2015-05-27 | 2018-10-16 | International Business Machines Corporation | User interface for a query answering system |
US10127220B2 (en) | 2015-06-04 | 2018-11-13 | Apple Inc. | Language identification from short strings |
US10101822B2 (en) | 2015-06-05 | 2018-10-16 | Apple Inc. | Language input correction |
US9578173B2 (en) | 2015-06-05 | 2017-02-21 | Apple Inc. | Virtual assistant aided communication with 3rd party service in a communication session |
US10255907B2 (en) | 2015-06-07 | 2019-04-09 | Apple Inc. | Automatic accent detection using acoustic models |
US11025565B2 (en) | 2015-06-07 | 2021-06-01 | Apple Inc. | Personalized prediction of responses for instant messaging |
US10186254B2 (en) | 2015-06-07 | 2019-01-22 | Apple Inc. | Context-based endpoint detection |
US10866994B2 (en) | 2015-06-23 | 2020-12-15 | Splunk Inc. | Systems and methods for instant crawling, curation of data sources, and enabling ad-hoc search |
US11042591B2 (en) | 2015-06-23 | 2021-06-22 | Splunk Inc. | Analytical search engine |
US10170014B2 (en) * | 2015-07-28 | 2019-01-01 | International Business Machines Corporation | Domain-specific question-answer pair generation |
US10747498B2 (en) | 2015-09-08 | 2020-08-18 | Apple Inc. | Zero latency digital assistant |
US10671428B2 (en) | 2015-09-08 | 2020-06-02 | Apple Inc. | Distributed personal assistant |
US10740384B2 (en) | 2015-09-08 | 2020-08-11 | Apple Inc. | Intelligent automated assistant for media search and playback |
US9697820B2 (en) | 2015-09-24 | 2017-07-04 | Apple Inc. | Unit-selection text-to-speech synthesis using concatenation-sensitive neural networks |
US10366158B2 (en) | 2015-09-29 | 2019-07-30 | Apple Inc. | Efficient word encoding for recurrent neural network language models |
US11010550B2 (en) | 2015-09-29 | 2021-05-18 | Apple Inc. | Unified language modeling framework for word prediction, auto-completion and auto-correction |
US11587559B2 (en) | 2015-09-30 | 2023-02-21 | Apple Inc. | Intelligent device identification |
WO2017059500A1 (en) * | 2015-10-09 | 2017-04-13 | Sayity Pty Ltd | Frameworks and methodologies configured to enable streamlined integration of natural language processing functionality with one or more user interface environments, including assisted learning process |
US10691473B2 (en) | 2015-11-06 | 2020-06-23 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US10049668B2 (en) | 2015-12-02 | 2018-08-14 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
CN106844400A (zh) * | 2015-12-07 | 2017-06-13 | 南京中兴新软件有限责任公司 | 智能应答方法及装置 |
US10146858B2 (en) | 2015-12-11 | 2018-12-04 | International Business Machines Corporation | Discrepancy handler for document ingestion into a corpus for a cognitive computing system |
US9940384B2 (en) | 2015-12-15 | 2018-04-10 | International Business Machines Corporation | Statistical clustering inferred from natural language to drive relevant analysis and conversation with users |
US10268756B2 (en) | 2015-12-18 | 2019-04-23 | Here Global B.V. | Method and apparatus for providing natural language input in a cartographic system |
US10223066B2 (en) | 2015-12-23 | 2019-03-05 | Apple Inc. | Proactive assistance based on dialog communication between devices |
US10176250B2 (en) | 2016-01-12 | 2019-01-08 | International Business Machines Corporation | Automated curation of documents in a corpus for a cognitive computing system |
US9842161B2 (en) * | 2016-01-12 | 2017-12-12 | International Business Machines Corporation | Discrepancy curator for documents in a corpus of a cognitive computing system |
US10446143B2 (en) | 2016-03-14 | 2019-10-15 | Apple Inc. | Identification of voice inputs providing credentials |
US10217464B2 (en) | 2016-05-13 | 2019-02-26 | Koninklijke Philips N.V. | Vocabulary generation system |
US9934775B2 (en) | 2016-05-26 | 2018-04-03 | Apple Inc. | Unit-selection text-to-speech synthesis based on predicted concatenation parameters |
US9972304B2 (en) | 2016-06-03 | 2018-05-15 | Apple Inc. | Privacy preserving distributed evaluation framework for embedded personalized systems |
US10249300B2 (en) | 2016-06-06 | 2019-04-02 | Apple Inc. | Intelligent list reading |
US10049663B2 (en) | 2016-06-08 | 2018-08-14 | Apple, Inc. | Intelligent automated assistant for media exploration |
DK179588B1 (en) | 2016-06-09 | 2019-02-22 | Apple Inc. | INTELLIGENT AUTOMATED ASSISTANT IN A HOME ENVIRONMENT |
US10586535B2 (en) | 2016-06-10 | 2020-03-10 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
US10490187B2 (en) | 2016-06-10 | 2019-11-26 | Apple Inc. | Digital assistant providing automated status report |
US10192552B2 (en) | 2016-06-10 | 2019-01-29 | Apple Inc. | Digital assistant providing whispered speech |
US10067938B2 (en) | 2016-06-10 | 2018-09-04 | Apple Inc. | Multilingual word prediction |
US10509862B2 (en) | 2016-06-10 | 2019-12-17 | Apple Inc. | Dynamic phrase expansion of language input |
DK179415B1 (en) | 2016-06-11 | 2018-06-14 | Apple Inc | Intelligent device arbitration and control |
DK201670540A1 (en) | 2016-06-11 | 2018-01-08 | Apple Inc | Application integration with a digital assistant |
DK179049B1 (en) | 2016-06-11 | 2017-09-18 | Apple Inc | Data driven natural language event detection and classification |
DK179343B1 (en) | 2016-06-11 | 2018-05-14 | Apple Inc | Intelligent task discovery |
US10318405B2 (en) * | 2016-08-24 | 2019-06-11 | International Business Machines Corporation | Applying consistent log levels to application log messages |
US10043516B2 (en) | 2016-09-23 | 2018-08-07 | Apple Inc. | Intelligent automated assistant |
US9842297B1 (en) * | 2016-09-29 | 2017-12-12 | International Business Machines Corporation | Establishing industry ground truth |
US9785717B1 (en) | 2016-09-29 | 2017-10-10 | International Business Machines Corporation | Intent based search result interaction |
CN106528531B (zh) * | 2016-10-31 | 2019-09-03 | 北京百度网讯科技有限公司 | 基于人工智能的意图分析方法及装置 |
US10593346B2 (en) | 2016-12-22 | 2020-03-17 | Apple Inc. | Rank-reduced token representation for automatic speech recognition |
US11392970B2 (en) * | 2017-02-15 | 2022-07-19 | Qualtrics, Llc | Administering a digital survey over voice-capable devices |
DK201770439A1 (en) | 2017-05-11 | 2018-12-13 | Apple Inc. | Offline personal assistant |
DK179496B1 (en) | 2017-05-12 | 2019-01-15 | Apple Inc. | USER-SPECIFIC Acoustic Models |
DK179745B1 (en) | 2017-05-12 | 2019-05-01 | Apple Inc. | SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT |
DK201770431A1 (en) | 2017-05-15 | 2018-12-20 | Apple Inc. | Optimizing dialogue policy decisions for digital assistants using implicit feedback |
DK201770432A1 (en) | 2017-05-15 | 2018-12-21 | Apple Inc. | Hierarchical belief states for digital assistants |
DK179560B1 (en) | 2017-05-16 | 2019-02-18 | Apple Inc. | FAR-FIELD EXTENSION FOR DIGITAL ASSISTANT SERVICES |
CN107193948B (zh) * | 2017-05-22 | 2018-04-20 | 邢加和 | 人机对话数据分析方法及装置 |
US10387515B2 (en) | 2017-06-08 | 2019-08-20 | International Business Machines Corporation | Network search query |
CN107220380A (zh) * | 2017-06-27 | 2017-09-29 | 北京百度网讯科技有限公司 | 基于人工智能的问答推荐方法、装置和计算机设备 |
US11315560B2 (en) | 2017-07-14 | 2022-04-26 | Cognigy Gmbh | Method for conducting dialog between human and computer |
US20190025906A1 (en) | 2017-07-21 | 2019-01-24 | Pearson Education, Inc. | Systems and methods for virtual reality-based assessment |
US11106683B2 (en) * | 2017-08-25 | 2021-08-31 | Accenture Global Solutions Limited | System architecture for interactive query processing |
US10552410B2 (en) | 2017-11-14 | 2020-02-04 | Mindbridge Analytics Inc. | Method and system for presenting a user selectable interface in response to a natural language request |
US11048878B2 (en) | 2018-05-02 | 2021-06-29 | International Business Machines Corporation | Determining answers to a question that includes multiple foci |
TWI665567B (zh) * | 2018-09-26 | 2019-07-11 | 華碩電腦股份有限公司 | 語意處理方法、電子裝置以及非暫態電腦可讀取記錄媒體 |
CN110968663B (zh) * | 2018-09-30 | 2023-05-23 | 北京国双科技有限公司 | 一种问答系统的答案展示方法及装置 |
US10970322B2 (en) * | 2018-11-26 | 2021-04-06 | International Business Machines Corporation | Training an artificial intelligence to generate an answer to a query based on an answer table pattern |
US20220237637A1 (en) * | 2018-12-18 | 2022-07-28 | Meta Platforms, Inc. | Systems and methods for real time crowdsourcing |
CN109710939B (zh) * | 2018-12-28 | 2023-06-09 | 北京百度网讯科技有限公司 | 用于确定主题的方法和装置 |
CN112580356A (zh) * | 2019-09-27 | 2021-03-30 | 华为技术有限公司 | 一种识别具有相同语义的问题的方法及电子设备 |
CN111681765B (zh) * | 2020-04-29 | 2023-08-11 | 华南师范大学 | 一种医学问答系统的多模型融合方法 |
US11823082B2 (en) | 2020-05-06 | 2023-11-21 | Kore.Ai, Inc. | Methods for orchestrating an automated conversation in one or more networks and devices thereof |
WO2022006135A1 (en) | 2020-06-29 | 2022-01-06 | 6Sense Insights, Inc. | Artificial intelligence for keyword recommendation |
US20220100756A1 (en) * | 2020-09-30 | 2022-03-31 | Microsoft Technology Licensing, Llc | Navigation agent for a search interface |
US20230060159A1 (en) * | 2021-08-14 | 2023-03-02 | Flipkart Internet Private Limited | System and method for generating a natural language answer for one or more user queries |
CN117215902B (zh) * | 2023-11-09 | 2024-03-08 | 北京集度科技有限公司 | 日志解析方法、装置、设备及存储介质 |
Citations (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5386556A (en) * | 1989-03-06 | 1995-01-31 | International Business Machines Corporation | Natural language analyzing apparatus and method |
US5418948A (en) * | 1991-10-08 | 1995-05-23 | West Publishing Company | Concept matching of natural language queries with a database of document concepts |
US5787234A (en) * | 1994-06-22 | 1998-07-28 | Molloy; Bruce G. | System and method for representing and retrieving knowledge in an adaptive cognitive network |
US5995921A (en) * | 1996-04-23 | 1999-11-30 | International Business Machines Corporation | Natural language help interface |
US6006225A (en) * | 1998-06-15 | 1999-12-21 | Amazon.Com | Refining search queries by the suggestion of correlated terms from prior searches |
US6028601A (en) * | 1997-04-01 | 2000-02-22 | Apple Computer, Inc. | FAQ link creation between user's questions and answers |
US6081774A (en) * | 1997-08-22 | 2000-06-27 | Novell, Inc. | Natural language information retrieval system and method |
US20010014852A1 (en) * | 1998-09-09 | 2001-08-16 | Tsourikov Valery M. | Document semantic analysis/selection with knowledge creativity capability |
US6346952B1 (en) * | 1999-12-01 | 2002-02-12 | Genesys Telecommunications Laboratories, Inc. | Method and apparatus for summarizing previous threads in a communication-center chat session |
US6560590B1 (en) * | 2000-02-14 | 2003-05-06 | Kana Software, Inc. | Method and apparatus for multiple tiered matching of natural language queries to positions in a text corpus |
US6584464B1 (en) * | 1999-03-19 | 2003-06-24 | Ask Jeeves, Inc. | Grammar template query system |
US20030208472A1 (en) * | 2000-04-11 | 2003-11-06 | Pham Peter Manh | Method and apparatus for transparent keyword-based hyperlink |
US6675159B1 (en) * | 2000-07-27 | 2004-01-06 | Science Applic Int Corp | Concept-based search and retrieval system |
US6687689B1 (en) * | 2000-06-16 | 2004-02-03 | Nusuara Technologies Sdn. Bhd. | System and methods for document retrieval using natural language-based queries |
Family Cites Families (20)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5864844A (en) * | 1993-02-18 | 1999-01-26 | Apple Computer, Inc. | System and method for enhancing a user interface with a computer based training tool |
US5454106A (en) * | 1993-05-17 | 1995-09-26 | International Business Machines Corporation | Database retrieval system using natural language for presenting understood components of an ambiguous query on a user interface |
TW256927B (US20040243568A1-20041202-P00025.png) | 1994-03-01 | 1995-09-11 | Hitachi Seisakusyo Kk | |
US6029195A (en) * | 1994-11-29 | 2000-02-22 | Herz; Frederick S. M. | System for customized electronic identification of desirable objects |
US5673369A (en) * | 1995-03-02 | 1997-09-30 | International Business Machines Corporation | Authoring knowledge-based systems using interactive directed graphs |
US6076088A (en) * | 1996-02-09 | 2000-06-13 | Paik; Woojin | Information extraction system and method using concept relation concept (CRC) triples |
US6182083B1 (en) * | 1997-11-17 | 2001-01-30 | Sun Microsystems, Inc. | Method and system for multi-entry and multi-template matching in a database |
US5920854A (en) * | 1996-08-14 | 1999-07-06 | Infoseek Corporation | Real-time document collection search engine with phrase indexing |
US5842221A (en) * | 1997-02-19 | 1998-11-24 | Wisdomware, Inc. | Dynamic frequently asked questions (FAQ) system |
US5926784A (en) * | 1997-07-17 | 1999-07-20 | Microsoft Corporation | Method and system for natural language parsing using podding |
US5933822A (en) * | 1997-07-22 | 1999-08-03 | Microsoft Corporation | Apparatus and methods for an information retrieval system that employs natural language processing of search results to improve overall precision |
KR100331299B1 (ko) * | 1997-08-30 | 2002-08-13 | 삼성전자 주식회사 | 고객지원탐색엔진시스템및그의데이터탐색방법 |
US5974412A (en) * | 1997-09-24 | 1999-10-26 | Sapient Health Network | Intelligent query system for automatically indexing information in a database and automatically categorizing users |
US6256648B1 (en) * | 1998-01-29 | 2001-07-03 | At&T Corp. | System and method for selecting and displaying hyperlinked information resources |
US6493699B2 (en) * | 1998-03-27 | 2002-12-10 | International Business Machines Corporation | Defining and characterizing an analysis space for precomputed views |
US6256623B1 (en) * | 1998-06-22 | 2001-07-03 | Microsoft Corporation | Network search access construct for accessing web-based search services |
US6370526B1 (en) * | 1999-05-18 | 2002-04-09 | International Business Machines Corporation | Self-adaptive method and system for providing a user-preferred ranking order of object sets |
US6405161B1 (en) * | 1999-07-26 | 2002-06-11 | Arch Development Corporation | Method and apparatus for learning the morphology of a natural language |
US6453315B1 (en) * | 1999-09-22 | 2002-09-17 | Applied Semantics, Inc. | Meaning-based information organization and retrieval |
US6434550B1 (en) * | 2000-04-14 | 2002-08-13 | Rightnow Technologies, Inc. | Temporal updates of relevancy rating of retrieved information in an information search system |
-
2000
- 2000-08-24 US US09/645,806 patent/US6766320B1/en not_active Expired - Lifetime
-
2004
- 2004-03-22 US US10/806,789 patent/US20040243568A1/en not_active Abandoned
Patent Citations (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5386556A (en) * | 1989-03-06 | 1995-01-31 | International Business Machines Corporation | Natural language analyzing apparatus and method |
US5418948A (en) * | 1991-10-08 | 1995-05-23 | West Publishing Company | Concept matching of natural language queries with a database of document concepts |
US5787234A (en) * | 1994-06-22 | 1998-07-28 | Molloy; Bruce G. | System and method for representing and retrieving knowledge in an adaptive cognitive network |
US5995921A (en) * | 1996-04-23 | 1999-11-30 | International Business Machines Corporation | Natural language help interface |
US6028601A (en) * | 1997-04-01 | 2000-02-22 | Apple Computer, Inc. | FAQ link creation between user's questions and answers |
US6081774A (en) * | 1997-08-22 | 2000-06-27 | Novell, Inc. | Natural language information retrieval system and method |
US6006225A (en) * | 1998-06-15 | 1999-12-21 | Amazon.Com | Refining search queries by the suggestion of correlated terms from prior searches |
US20010014852A1 (en) * | 1998-09-09 | 2001-08-16 | Tsourikov Valery M. | Document semantic analysis/selection with knowledge creativity capability |
US6584464B1 (en) * | 1999-03-19 | 2003-06-24 | Ask Jeeves, Inc. | Grammar template query system |
US6346952B1 (en) * | 1999-12-01 | 2002-02-12 | Genesys Telecommunications Laboratories, Inc. | Method and apparatus for summarizing previous threads in a communication-center chat session |
US6560590B1 (en) * | 2000-02-14 | 2003-05-06 | Kana Software, Inc. | Method and apparatus for multiple tiered matching of natural language queries to positions in a text corpus |
US20030208472A1 (en) * | 2000-04-11 | 2003-11-06 | Pham Peter Manh | Method and apparatus for transparent keyword-based hyperlink |
US6687689B1 (en) * | 2000-06-16 | 2004-02-03 | Nusuara Technologies Sdn. Bhd. | System and methods for document retrieval using natural language-based queries |
US6675159B1 (en) * | 2000-07-27 | 2004-01-06 | Science Applic Int Corp | Concept-based search and retrieval system |
Cited By (312)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0757798A (ja) * | 1993-08-13 | 1995-03-03 | Sumitomo 3M Ltd | 電線接続部の被覆チューブ |
US20020049756A1 (en) * | 2000-10-11 | 2002-04-25 | Microsoft Corporation | System and method for searching multiple disparate search engines |
US7451136B2 (en) * | 2000-10-11 | 2008-11-11 | Microsoft Corporation | System and method for searching multiple disparate search engines |
US20060136455A1 (en) * | 2001-10-12 | 2006-06-22 | Microsoft Corporation | Clustering Web Queries |
US7149732B2 (en) | 2001-10-12 | 2006-12-12 | Microsoft Corporation | Clustering web queries |
US20030144994A1 (en) * | 2001-10-12 | 2003-07-31 | Ji-Rong Wen | Clustering web queries |
US7523105B2 (en) | 2001-10-12 | 2009-04-21 | Microsoft Corporation | Clustering web queries |
US7287025B2 (en) * | 2003-02-12 | 2007-10-23 | Microsoft Corporation | Systems and methods for query expansion |
US20040158560A1 (en) * | 2003-02-12 | 2004-08-12 | Ji-Rong Wen | Systems and methods for query expansion |
US8666983B2 (en) * | 2003-06-13 | 2014-03-04 | Microsoft Corporation | Architecture for generating responses to search engine queries |
US20040254917A1 (en) * | 2003-06-13 | 2004-12-16 | Brill Eric D. | Architecture for generating responses to search engine queries |
US8090086B2 (en) | 2003-09-26 | 2012-01-03 | At&T Intellectual Property I, L.P. | VoiceXML and rule engine based switchboard for interactive voice response (IVR) services |
US20070156625A1 (en) * | 2004-01-06 | 2007-07-05 | Neuric Technologies, Llc | Method for movie animation |
US7089218B1 (en) * | 2004-01-06 | 2006-08-08 | Neuric Technologies, Llc | Method for inclusion of psychological temperament in an electronic emulation of the human brain |
US7925492B2 (en) | 2004-01-06 | 2011-04-12 | Neuric Technologies, L.L.C. | Method for determining relationships through use of an ordered list between processing nodes in an emulated human brain |
US20080243741A1 (en) * | 2004-01-06 | 2008-10-02 | Neuric Technologies, Llc | Method and apparatus for defining an artificial brain via a plurality of concept nodes connected together through predetermined relationships |
US9064211B2 (en) | 2004-01-06 | 2015-06-23 | Neuric Technologies, Llc | Method for determining relationships through use of an ordered list between processing nodes in an emulated human brain |
US20100042568A1 (en) * | 2004-01-06 | 2010-02-18 | Neuric Technologies, Llc | Electronic brain model with neuron reinforcement |
US8001067B2 (en) | 2004-01-06 | 2011-08-16 | Neuric Technologies, Llc | Method for substituting an electronic emulation of the human brain into an application to replace a human |
US9213936B2 (en) | 2004-01-06 | 2015-12-15 | Neuric, Llc | Electronic brain model with neuron tables |
US8725493B2 (en) | 2004-01-06 | 2014-05-13 | Neuric Llc | Natural language parsing method to provide conceptual flow |
US7849034B2 (en) | 2004-01-06 | 2010-12-07 | Neuric Technologies, Llc | Method of emulating human cognition in a brain model containing a plurality of electronically represented neurons |
US20080228467A1 (en) * | 2004-01-06 | 2008-09-18 | Neuric Technologies, Llc | Natural language parsing method to provide conceptual flow |
US20070250464A1 (en) * | 2004-01-06 | 2007-10-25 | Neuric Technologies, Llc | Historical figures in today's society |
US20080300841A1 (en) * | 2004-01-06 | 2008-12-04 | Neuric Technologies, Llc | Method for inclusion of psychological temperament in an electronic emulation of the human brain |
US20070282765A1 (en) * | 2004-01-06 | 2007-12-06 | Neuric Technologies, Llc | Method for substituting an electronic emulation of the human brain into an application to replace a human |
US20070288406A1 (en) * | 2004-01-06 | 2007-12-13 | Neuric Technologies, Llc | Method for determining relationships through use of an ordered list between processing nodes in an emulated human brain |
US20050203934A1 (en) * | 2004-03-09 | 2005-09-15 | Microsoft Corporation | Compression of logs of language data |
US7936861B2 (en) | 2004-07-23 | 2011-05-03 | At&T Intellectual Property I, L.P. | Announcement system and method of use |
US8165281B2 (en) | 2004-07-28 | 2012-04-24 | At&T Intellectual Property I, L.P. | Method and system for mapping caller information to call center agent transactions |
US9368111B2 (en) | 2004-08-12 | 2016-06-14 | Interactions Llc | System and method for targeted tuning of a speech recognition system |
US8751232B2 (en) | 2004-08-12 | 2014-06-10 | At&T Intellectual Property I, L.P. | System and method for targeted tuning of a speech recognition system |
US8401851B2 (en) | 2004-08-12 | 2013-03-19 | At&T Intellectual Property I, L.P. | System and method for targeted tuning of a speech recognition system |
US9639878B2 (en) | 2004-08-31 | 2017-05-02 | Semantic Search Technologies LLC a Texas Limited Liability Company | Computer-aided extraction of semantics from keywords to confirm match of buyer offers to seller bids |
US9378521B2 (en) | 2004-08-31 | 2016-06-28 | Semantic Search Technologies Llc A California Limited Liability Company | Computer-aided extraction of semantics from keywords to confirm match of buyer offers to seller bids |
US8244726B1 (en) | 2004-08-31 | 2012-08-14 | Bruce Matesso | Computer-aided extraction of semantics from keywords to confirm match of buyer offers to seller bids |
US9069860B2 (en) | 2004-08-31 | 2015-06-30 | Semantic Search Technologies Llc | Computer-aided extraction of semantics from keywords to confirm match of buyer offers to seller bids |
US8660256B2 (en) | 2004-10-05 | 2014-02-25 | At&T Intellectual Property, L.P. | Dynamic load balancing between multiple locations with different telephony system |
US8102992B2 (en) | 2004-10-05 | 2012-01-24 | At&T Intellectual Property, L.P. | Dynamic load balancing between multiple locations with different telephony system |
US20060085401A1 (en) * | 2004-10-20 | 2006-04-20 | Microsoft Corporation | Analyzing operational and other data from search system or the like |
US8321446B2 (en) | 2004-10-27 | 2012-11-27 | At&T Intellectual Property I, L.P. | Method and system to combine keyword results and natural language search results |
US9047377B2 (en) | 2004-10-27 | 2015-06-02 | At&T Intellectual Property I, L.P. | Method and system to combine keyword and natural language search results |
US8667005B2 (en) | 2004-10-27 | 2014-03-04 | At&T Intellectual Property I, L.P. | Method and system to combine keyword and natural language search results |
US7668889B2 (en) | 2004-10-27 | 2010-02-23 | At&T Intellectual Property I, Lp | Method and system to combine keyword and natural language search results |
US7657005B2 (en) | 2004-11-02 | 2010-02-02 | At&T Intellectual Property I, L.P. | System and method for identifying telephone callers |
US7724889B2 (en) | 2004-11-29 | 2010-05-25 | At&T Intellectual Property I, L.P. | System and method for utilizing confidence levels in automated call routing |
US9112972B2 (en) | 2004-12-06 | 2015-08-18 | Interactions Llc | System and method for processing speech |
US7720203B2 (en) | 2004-12-06 | 2010-05-18 | At&T Intellectual Property I, L.P. | System and method for processing speech |
US7864942B2 (en) | 2004-12-06 | 2011-01-04 | At&T Intellectual Property I, L.P. | System and method for routing calls |
US9350862B2 (en) | 2004-12-06 | 2016-05-24 | Interactions Llc | System and method for processing speech |
US8306192B2 (en) | 2004-12-06 | 2012-11-06 | At&T Intellectual Property I, L.P. | System and method for processing speech |
US20100185437A1 (en) * | 2005-01-06 | 2010-07-22 | Neuric Technologies, Llc | Process of dialogue and discussion |
US8473449B2 (en) | 2005-01-06 | 2013-06-25 | Neuric Technologies, Llc | Process of dialogue and discussion |
US9088652B2 (en) | 2005-01-10 | 2015-07-21 | At&T Intellectual Property I, L.P. | System and method for speech-enabled call routing |
US7751551B2 (en) | 2005-01-10 | 2010-07-06 | At&T Intellectual Property I, L.P. | System and method for speech-enabled call routing |
US8503662B2 (en) | 2005-01-10 | 2013-08-06 | At&T Intellectual Property I, L.P. | System and method for speech-enabled call routing |
US8824659B2 (en) | 2005-01-10 | 2014-09-02 | At&T Intellectual Property I, L.P. | System and method for speech-enabled call routing |
US7966176B2 (en) | 2005-01-14 | 2011-06-21 | At&T Intellectual Property I, L.P. | System and method for independently recognizing and selecting actions and objects in a speech recognition system |
US8068596B2 (en) | 2005-02-04 | 2011-11-29 | At&T Intellectual Property I, L.P. | Call center system for multiple transaction selections |
US20080195378A1 (en) * | 2005-02-08 | 2008-08-14 | Nec Corporation | Question and Answer Data Editing Device, Question and Answer Data Editing Method and Question Answer Data Editing Program |
US8983962B2 (en) * | 2005-02-08 | 2015-03-17 | Nec Corporation | Question and answer data editing device, question and answer data editing method and question answer data editing program |
US20060184517A1 (en) * | 2005-02-15 | 2006-08-17 | Microsoft Corporation | Answers analytics: computing answers across discrete data |
US7792811B2 (en) | 2005-02-16 | 2010-09-07 | Transaxtions Llc | Intelligent search with guiding info |
US20080104047A1 (en) * | 2005-02-16 | 2008-05-01 | Transaxtions Llc | Intelligent search with guiding info |
US7895167B2 (en) * | 2005-02-16 | 2011-02-22 | Xpolog Ltd. | System and method for analysis and management of logs and events |
US20060184529A1 (en) * | 2005-02-16 | 2006-08-17 | Gal Berg | System and method for analysis and management of logs and events |
US8130936B2 (en) | 2005-03-03 | 2012-03-06 | At&T Intellectual Property I, L.P. | System and method for on hold caller-controlled activities and entertainment |
US7574436B2 (en) * | 2005-03-10 | 2009-08-11 | Yahoo! Inc. | Reranking and increasing the relevance of the results of Internet searches |
US20060206476A1 (en) * | 2005-03-10 | 2006-09-14 | Yahoo!, Inc. | Reranking and increasing the relevance of the results of Internet searches |
US7933399B2 (en) | 2005-03-22 | 2011-04-26 | At&T Intellectual Property I, L.P. | System and method for utilizing virtual agents in an interactive voice response application |
US8223954B2 (en) | 2005-03-22 | 2012-07-17 | At&T Intellectual Property I, L.P. | System and method for automating customer relations in a communications environment |
US8488770B2 (en) | 2005-03-22 | 2013-07-16 | At&T Intellectual Property I, L.P. | System and method for automating customer relations in a communications environment |
US8295469B2 (en) | 2005-05-13 | 2012-10-23 | At&T Intellectual Property I, L.P. | System and method of determining call treatment of repeat calls |
US8879714B2 (en) | 2005-05-13 | 2014-11-04 | At&T Intellectual Property I, L.P. | System and method of determining call treatment of repeat calls |
US20090307194A1 (en) * | 2005-06-03 | 2009-12-10 | Delefevre Patrick Y | Neutral sales consultant |
US8280030B2 (en) | 2005-06-03 | 2012-10-02 | At&T Intellectual Property I, Lp | Call routing system and method of using the same |
US8005204B2 (en) | 2005-06-03 | 2011-08-23 | At&T Intellectual Property I, L.P. | Call routing system and method of using the same |
US8619966B2 (en) | 2005-06-03 | 2013-12-31 | At&T Intellectual Property I, L.P. | Call routing system and method of using the same |
US9729719B2 (en) | 2005-07-01 | 2017-08-08 | At&T Intellectual Property I, L.P. | System and method of automated order status retrieval |
US20070005343A1 (en) * | 2005-07-01 | 2007-01-04 | Xerox Corporation | Concept matching |
US8731165B2 (en) | 2005-07-01 | 2014-05-20 | At&T Intellectual Property I, L.P. | System and method of automated order status retrieval |
US9088657B2 (en) | 2005-07-01 | 2015-07-21 | At&T Intellectual Property I, L.P. | System and method of automated order status retrieval |
US7689411B2 (en) * | 2005-07-01 | 2010-03-30 | Xerox Corporation | Concept matching |
US20070025528A1 (en) * | 2005-07-07 | 2007-02-01 | Sbc Knowledge Ventures, L.P. | System and method for automated performance monitoring for a call servicing system |
US8175253B2 (en) | 2005-07-07 | 2012-05-08 | At&T Intellectual Property I, L.P. | System and method for automated performance monitoring for a call servicing system |
US20130275121A1 (en) * | 2005-08-01 | 2013-10-17 | Evi Technologies Limited | Knowledge repository |
US9098492B2 (en) | 2005-08-01 | 2015-08-04 | Amazon Technologies, Inc. | Knowledge repository |
US8526577B2 (en) | 2005-08-25 | 2013-09-03 | At&T Intellectual Property I, L.P. | System and method to access content from a speech-enabled automated system |
US8548157B2 (en) | 2005-08-29 | 2013-10-01 | At&T Intellectual Property I, L.P. | System and method of managing incoming telephone calls at a call center |
US7627559B2 (en) * | 2005-12-15 | 2009-12-01 | Microsoft Corporation | Context-based key phrase discovery and similarity measurement utilizing search engine query logs |
US20070143278A1 (en) * | 2005-12-15 | 2007-06-21 | Microsoft Corporation | Context-based key phrase discovery and similarity measurement utilizing search engine query logs |
US20070192313A1 (en) * | 2006-01-27 | 2007-08-16 | William Derek Finley | Data search method with statistical analysis performed on user provided ratings of the initial search results |
US20070208706A1 (en) * | 2006-03-06 | 2007-09-06 | Anand Madhavan | Vertical search expansion, disambiguation, and optimization of search queries |
WO2007103237A2 (en) * | 2006-03-06 | 2007-09-13 | Yahoo! Inc. | Vertical search expansion, disambiguation, and optimization of search queries |
US20070208724A1 (en) * | 2006-03-06 | 2007-09-06 | Anand Madhavan | Vertical search expansion, disambiguation, and optimization of search queries |
US7805441B2 (en) | 2006-03-06 | 2010-09-28 | Yahoo! Inc. | Vertical search expansion, disambiguation, and optimization of search queries |
WO2007103237A3 (en) * | 2006-03-06 | 2007-11-22 | Yahoo Inc | Vertical search expansion, disambiguation, and optimization of search queries |
US8832097B2 (en) | 2006-03-06 | 2014-09-09 | Yahoo! Inc. | Vertical search expansion, disambiguation, and optimization of search queries |
US20090112828A1 (en) * | 2006-03-13 | 2009-04-30 | Answers Corporation | Method and system for answer extraction |
WO2007108788A2 (en) * | 2006-03-13 | 2007-09-27 | Answers Corporation | Method and system for answer extraction |
WO2007108788A3 (en) * | 2006-03-13 | 2009-06-11 | Answers Corp | Method and system for answer extraction |
US7716229B1 (en) | 2006-03-31 | 2010-05-11 | Microsoft Corporation | Generating misspells from query log context usage |
US20080022211A1 (en) * | 2006-07-24 | 2008-01-24 | Chacha Search, Inc. | Method, system, and computer readable storage for podcasting and video training in an information search system |
US8327270B2 (en) * | 2006-07-24 | 2012-12-04 | Chacha Search, Inc. | Method, system, and computer readable storage for podcasting and video training in an information search system |
US20080082485A1 (en) * | 2006-09-28 | 2008-04-03 | Microsoft Corporation | Personalized information retrieval search with backoff |
US7783636B2 (en) | 2006-09-28 | 2010-08-24 | Microsoft Corporation | Personalized information retrieval search with backoff |
US11816114B1 (en) * | 2006-11-02 | 2023-11-14 | Google Llc | Modifying search result ranking based on implicit user feedback |
US10229166B1 (en) * | 2006-11-02 | 2019-03-12 | Google Llc | Modifying search result ranking based on implicit user feedback |
US9811566B1 (en) * | 2006-11-02 | 2017-11-07 | Google Inc. | Modifying search result ranking based on implicit user feedback |
US11188544B1 (en) * | 2006-11-02 | 2021-11-30 | Google Llc | Modifying search result ranking based on implicit user feedback |
US9262784B2 (en) | 2007-04-16 | 2016-02-16 | Ebay Inc. | Method, medium, and system for comparison shopping |
US11030662B2 (en) | 2007-04-16 | 2021-06-08 | Ebay Inc. | Visualization of reputation ratings |
US9613375B2 (en) | 2007-04-16 | 2017-04-04 | Paypal, Inc. | Distributed commerce application-widget |
US20080255957A1 (en) * | 2007-04-16 | 2008-10-16 | Ebay Inc, | System and method for online item publication and marketplace within virtual worlds |
US20080256040A1 (en) * | 2007-04-16 | 2008-10-16 | Ebay Inc. | Visualization of reputation ratings |
US8977631B2 (en) * | 2007-04-16 | 2015-03-10 | Ebay Inc. | Visualization of reputation ratings |
US11763356B2 (en) | 2007-04-16 | 2023-09-19 | Ebay Inc. | Visualization of reputation ratings |
US20080255967A1 (en) * | 2007-04-16 | 2008-10-16 | Ebay Inc | System and method for comparison shopping |
US10127583B2 (en) | 2007-04-16 | 2018-11-13 | Ebay Inc. | Visualization of reputation ratings |
US9519681B2 (en) | 2007-10-04 | 2016-12-13 | Amazon Technologies, Inc. | Enhanced knowledge repository |
US20090276469A1 (en) * | 2008-05-01 | 2009-11-05 | International Business Machines Corporation | Method for transactional behavior extaction in distributed applications |
US20100088262A1 (en) * | 2008-09-29 | 2010-04-08 | Neuric Technologies, Llc | Emulated brain |
US20100114878A1 (en) * | 2008-10-22 | 2010-05-06 | Yumao Lu | Selective term weighting for web search based on automatic semantic parsing |
US20100145923A1 (en) * | 2008-12-04 | 2010-06-10 | Microsoft Corporation | Relaxed filter set |
US20110246496A1 (en) * | 2008-12-11 | 2011-10-06 | Chung Hee Sung | Information search method and information provision method based on user's intention |
US8554540B2 (en) * | 2008-12-11 | 2013-10-08 | Electronics And Telecommunication Research Institute | Topic map based indexing and searching apparatus |
US20100153094A1 (en) * | 2008-12-11 | 2010-06-17 | Electronics And Telecommunications Research Institute | Topic map based indexing and searching apparatus |
CN102246164A (zh) * | 2008-12-11 | 2011-11-16 | 有限公司呢哦派豆 | 基于用户意图的信息搜索方法以及信息提供方法 |
US9256679B2 (en) * | 2008-12-11 | 2016-02-09 | Neopad, Inc. | Information search method and system, information provision method and system based on user's intention |
US11182381B2 (en) | 2009-02-10 | 2021-11-23 | Amazon Technologies, Inc. | Local business and product search system and method |
US9805089B2 (en) | 2009-02-10 | 2017-10-31 | Amazon Technologies, Inc. | Local business and product search system and method |
US20160210301A1 (en) * | 2009-02-13 | 2016-07-21 | Microsoft Technology Licensing, Llc | Context-Aware Query Suggestion by Mining Log Data |
US8463720B1 (en) | 2009-03-27 | 2013-06-11 | Neuric Technologies, Llc | Method and apparatus for defining an artificial brain via a plurality of concept nodes defined by frame semantics |
US20100306214A1 (en) * | 2009-05-28 | 2010-12-02 | Microsoft Corporation | Identifying modifiers in web queries over structured data |
US9697259B1 (en) | 2009-08-31 | 2017-07-04 | Google Inc. | Refining search results |
CN101808003A (zh) * | 2010-02-11 | 2010-08-18 | 候万春 | 提供即时类型通信的客户端和系统以及方法 |
US11074257B2 (en) | 2010-04-19 | 2021-07-27 | Facebook, Inc. | Filtering search results for structured search queries |
US10282354B2 (en) | 2010-04-19 | 2019-05-07 | Facebook, Inc. | Detecting social graph elements for structured search queries |
US9959318B2 (en) | 2010-04-19 | 2018-05-01 | Facebook, Inc. | Default structured search queries on online social networks |
US10706481B2 (en) | 2010-04-19 | 2020-07-07 | Facebook, Inc. | Personalizing default search queries on online social networks |
US10331748B2 (en) | 2010-04-19 | 2019-06-25 | Facebook, Inc. | Dynamically generating recommendations based on social graph information |
US9465848B2 (en) | 2010-04-19 | 2016-10-11 | Facebook, Inc. | Detecting social graph elements for structured search queries |
US10430425B2 (en) | 2010-04-19 | 2019-10-01 | Facebook, Inc. | Generating suggested queries based on social graph information |
US10430477B2 (en) | 2010-04-19 | 2019-10-01 | Facebook, Inc. | Personalized structured search queries for online social networks |
US10282377B2 (en) | 2010-04-19 | 2019-05-07 | Facebook, Inc. | Suggested terms for ambiguous search queries |
US9514218B2 (en) | 2010-04-19 | 2016-12-06 | Facebook, Inc. | Ambiguous structured search queries on online social networks |
US10614084B2 (en) | 2010-04-19 | 2020-04-07 | Facebook, Inc. | Default suggested queries on online social networks |
US10275405B2 (en) | 2010-04-19 | 2019-04-30 | Facebook, Inc. | Automatically generating suggested queries in a social network environment |
US9342623B2 (en) | 2010-04-19 | 2016-05-17 | Facebook, Inc. | Automatically generating nodes and edges in an integrated social graph |
US10140338B2 (en) | 2010-04-19 | 2018-11-27 | Facebook, Inc. | Filtering structured search queries based on privacy settings |
US20110276535A1 (en) * | 2010-05-05 | 2011-11-10 | Salesforce.Com, Inc. | Knowledge article workflow management |
US11132610B2 (en) | 2010-05-14 | 2021-09-28 | Amazon Technologies, Inc. | Extracting structured knowledge from unstructured text |
US9110882B2 (en) | 2010-05-14 | 2015-08-18 | Amazon Technologies, Inc. | Extracting structured knowledge from unstructured text |
US8489525B2 (en) | 2010-05-20 | 2013-07-16 | International Business Machines Corporation | Automatic model evolution |
US8577818B2 (en) | 2010-05-20 | 2013-11-05 | International Business Machines Corporation | Automatic model evolution |
US8655901B1 (en) | 2010-06-23 | 2014-02-18 | Google Inc. | Translation-based query pattern mining |
US8977624B2 (en) | 2010-08-30 | 2015-03-10 | Microsoft Technology Licensing, Llc | Enhancing search-result relevance ranking using uniform resource locators for queries containing non-encoding characters |
US8452747B2 (en) * | 2010-09-07 | 2013-05-28 | Yahoo! Inc. | Building content in Q and A sites by auto-posting of questions extracted from web search logs |
US20120059816A1 (en) * | 2010-09-07 | 2012-03-08 | Priyesh Narayanan | Building content in q&a sites by auto-posting of questions extracted from web search logs |
US10678872B2 (en) * | 2010-09-10 | 2020-06-09 | Veveo, Inc. | Method of and system for conducting personalized federated search and presentation of results therefrom |
US20190065602A1 (en) * | 2010-09-10 | 2019-02-28 | Veveo, Inc. | Method of and system for conducting personalized federated search and presentation of results therefrom |
US8473486B2 (en) * | 2010-12-08 | 2013-06-25 | Microsoft Corporation | Training parsers to approximately optimize NDCG |
US20120150836A1 (en) * | 2010-12-08 | 2012-06-14 | Microsoft Corporation | Training parsers to approximately optimize ndcg |
US9218390B2 (en) | 2011-07-29 | 2015-12-22 | Yellowpages.Com Llc | Query parser derivation computing device and method for making a query parser for parsing unstructured search queries |
CN102999496A (zh) * | 2011-09-09 | 2013-03-27 | 北京百度网讯科技有限公司 | 建立需求分析模板的方法、搜索需求识别的方法及装置 |
US9679025B2 (en) * | 2012-05-25 | 2017-06-13 | International Business Machines Corporation | Providing search query results based on entity variant generation and normalization |
US20150220608A1 (en) * | 2012-05-25 | 2015-08-06 | International Business Machines Corporation | Providing search query results based on entity variant generation and normalization |
US9424233B2 (en) | 2012-07-20 | 2016-08-23 | Veveo, Inc. | Method of and system for inferring user intent in search input in a conversational interaction system |
US9183183B2 (en) | 2012-07-20 | 2015-11-10 | Veveo, Inc. | Method of and system for inferring user intent in search input in a conversational interaction system |
US9477643B2 (en) * | 2012-07-20 | 2016-10-25 | Veveo, Inc. | Method of and system for using conversation state information in a conversational interaction system |
US20140058724A1 (en) * | 2012-07-20 | 2014-02-27 | Veveo, Inc. | Method of and System for Using Conversation State Information in a Conversational Interaction System |
US9753993B2 (en) | 2012-07-27 | 2017-09-05 | Facebook, Inc. | Social static ranking for search |
US9465833B2 (en) | 2012-07-31 | 2016-10-11 | Veveo, Inc. | Disambiguating user intent in conversational interaction system for large corpus information retrieval |
US9336297B2 (en) * | 2012-08-02 | 2016-05-10 | Paypal, Inc. | Content inversion for user searches and product recommendations systems and methods |
US10402411B2 (en) | 2012-08-02 | 2019-09-03 | Paypal, Inc. | Content inversion for user searches and product recommendations systems and methods |
US11698908B2 (en) | 2012-08-02 | 2023-07-11 | Paypal, Inc. | Content inversion for user searches and product recommendations systems and methods |
US9223876B2 (en) * | 2012-10-11 | 2015-12-29 | Go Daddy Operating Company, LLC | Optimizing search engine ranking by recommending content including frequently searched questions |
US20150088850A1 (en) * | 2012-10-11 | 2015-03-26 | Go Daddy Operating Company, LLC | Optimizing search engine ranking by recommending content including frequently searched questions |
US8938438B2 (en) * | 2012-10-11 | 2015-01-20 | Go Daddy Operating Company, LLC | Optimizing search engine ranking by recommending content including frequently searched questions |
US9466295B2 (en) * | 2012-12-31 | 2016-10-11 | Via Technologies, Inc. | Method for correcting a speech response and natural language dialogue system |
US20140188477A1 (en) * | 2012-12-31 | 2014-07-03 | Via Technologies, Inc. | Method for correcting a speech response and natural language dialogue system |
US10244042B2 (en) | 2013-02-25 | 2019-03-26 | Facebook, Inc. | Pushing suggested search queries to mobile devices |
US10482427B2 (en) | 2013-03-14 | 2019-11-19 | Worldone, Inc. | System and method for concept discovery with online information environments |
WO2014159187A2 (en) * | 2013-03-14 | 2014-10-02 | Worldone, Inc. | System and method for concept discovery with online information environments |
WO2014159187A3 (en) * | 2013-03-14 | 2014-12-04 | Worldone, Inc. | Concept discovery with online information environments |
US10102245B2 (en) | 2013-04-25 | 2018-10-16 | Facebook, Inc. | Variable search query vertical access |
US10121493B2 (en) | 2013-05-07 | 2018-11-06 | Veveo, Inc. | Method of and system for real time feedback in an incremental speech input interface |
US9594852B2 (en) | 2013-05-08 | 2017-03-14 | Facebook, Inc. | Filtering suggested structured queries on online social networks |
US9715596B2 (en) | 2013-05-08 | 2017-07-25 | Facebook, Inc. | Approximate privacy indexing for search queries on online social networks |
US10108676B2 (en) | 2013-05-08 | 2018-10-23 | Facebook, Inc. | Filtering suggested queries on online social networks |
US10235359B2 (en) * | 2013-07-15 | 2019-03-19 | Nuance Communications, Inc. | Ontology and annotation driven grammar inference |
US20150019202A1 (en) * | 2013-07-15 | 2015-01-15 | Nuance Communications, Inc. | Ontology and Annotation Driven Grammar Inference |
US10032186B2 (en) | 2013-07-23 | 2018-07-24 | Facebook, Inc. | Native application testing |
US9514230B2 (en) * | 2013-07-30 | 2016-12-06 | Facebook, Inc. | Rewriting search queries on online social networks |
US9753992B2 (en) | 2013-07-30 | 2017-09-05 | Facebook, Inc. | Static rankings for search queries on online social networks |
US10324928B2 (en) | 2013-07-30 | 2019-06-18 | Facebook, Inc. | Rewriting search queries on online social networks |
US10255331B2 (en) * | 2013-07-30 | 2019-04-09 | Facebook, Inc. | Static rankings for search queries on online social networks |
US20150039597A1 (en) * | 2013-07-30 | 2015-02-05 | Facebook, Inc. | Rewriting Search Queries on Online Social Networks |
JP2016531355A (ja) * | 2013-07-30 | 2016-10-06 | フェイスブック,インク. | オンライン・ソーシャル・ネットワークにおける検索クエリの書き換え |
US9898554B2 (en) * | 2013-11-18 | 2018-02-20 | Google Inc. | Implicit question query identification |
US20150142851A1 (en) * | 2013-11-18 | 2015-05-21 | Google Inc. | Implicit Question Query Identification |
US9536522B1 (en) * | 2013-12-30 | 2017-01-03 | Google Inc. | Training a natural language processing model with information retrieval model annotations |
US9720956B2 (en) | 2014-01-17 | 2017-08-01 | Facebook, Inc. | Client-side search templates for online social networks |
US9465878B2 (en) | 2014-01-17 | 2016-10-11 | Go Daddy Operating Company, LLC | System and method for depicting backlink metrics for a website |
US20150213462A1 (en) * | 2014-01-24 | 2015-07-30 | Go Daddy Operating Company, LLC | Highlighting business trends |
US10713240B2 (en) | 2014-03-10 | 2020-07-14 | Interana, Inc. | Systems and methods for rapid data analysis |
US9734202B2 (en) | 2014-03-10 | 2017-08-15 | Interana, Inc. | Systems and methods for rapid data analysis |
US11372851B2 (en) | 2014-03-10 | 2022-06-28 | Scuba Analytics, Inc. | Systems and methods for rapid data analysis |
WO2015138497A3 (en) * | 2014-03-10 | 2015-12-03 | Interana, Inc. | Systems and methods for rapid data analysis |
US9323809B2 (en) | 2014-03-10 | 2016-04-26 | Interana, Inc. | System and methods for rapid data analysis |
US20160078012A1 (en) * | 2014-09-11 | 2016-03-17 | Bmc Software, Inc. | Systems and methods for formless information technology and social support mechanics |
US9852136B2 (en) | 2014-12-23 | 2017-12-26 | Rovi Guides, Inc. | Systems and methods for determining whether a negation statement applies to a current or past query |
US10585901B2 (en) | 2015-01-02 | 2020-03-10 | International Business Machines Corporation | Tailoring question answer results to personality traits |
US10341447B2 (en) | 2015-01-30 | 2019-07-02 | Rovi Guides, Inc. | Systems and methods for resolving ambiguous terms in social chatter based on a user profile |
US9854049B2 (en) | 2015-01-30 | 2017-12-26 | Rovi Guides, Inc. | Systems and methods for resolving ambiguous terms in social chatter based on a user profile |
US10747767B2 (en) | 2015-02-12 | 2020-08-18 | Interana, Inc. | Methods for enhancing rapid data analysis |
US11263215B2 (en) | 2015-02-12 | 2022-03-01 | Scuba Analytics, Inc. | Methods for enhancing rapid data analysis |
US10296507B2 (en) | 2015-02-12 | 2019-05-21 | Interana, Inc. | Methods for enhancing rapid data analysis |
CN104881446A (zh) * | 2015-05-14 | 2015-09-02 | 百度在线网络技术(北京)有限公司 | 搜索方法及装置 |
US10810217B2 (en) | 2015-10-07 | 2020-10-20 | Facebook, Inc. | Optionalization and fuzzy search on online social networks |
US10270868B2 (en) | 2015-11-06 | 2019-04-23 | Facebook, Inc. | Ranking of place-entities on online social networks |
US10795936B2 (en) | 2015-11-06 | 2020-10-06 | Facebook, Inc. | Suppressing entity suggestions on online social networks |
US9602965B1 (en) | 2015-11-06 | 2017-03-21 | Facebook, Inc. | Location-based place determination using online social networks |
US10003922B2 (en) | 2015-11-06 | 2018-06-19 | Facebook, Inc. | Location-based place determination using online social networks |
US10534814B2 (en) | 2015-11-11 | 2020-01-14 | Facebook, Inc. | Generating snippets on online social networks |
US10387511B2 (en) | 2015-11-25 | 2019-08-20 | Facebook, Inc. | Text-to-media indexes on online social networks |
US11074309B2 (en) | 2015-11-25 | 2021-07-27 | Facebook, Inc | Text-to-media indexes on online social networks |
US10740368B2 (en) | 2015-12-29 | 2020-08-11 | Facebook, Inc. | Query-composition platforms on online social networks |
US10915509B2 (en) | 2016-01-11 | 2021-02-09 | Facebook, Inc. | Identification of low-quality place-entities on online social networks |
US10019466B2 (en) | 2016-01-11 | 2018-07-10 | Facebook, Inc. | Identification of low-quality place-entities on online social networks |
US10853335B2 (en) | 2016-01-11 | 2020-12-01 | Facebook, Inc. | Identification of real-best-pages on online social networks |
US10282434B2 (en) | 2016-01-11 | 2019-05-07 | Facebook, Inc. | Suppression and deduplication of place-entities on online social networks |
US11100062B2 (en) | 2016-01-11 | 2021-08-24 | Facebook, Inc. | Suppression and deduplication of place-entities on online social networks |
US10162899B2 (en) | 2016-01-15 | 2018-12-25 | Facebook, Inc. | Typeahead intent icons and snippets on online social networks |
US10262039B1 (en) | 2016-01-15 | 2019-04-16 | Facebook, Inc. | Proximity-based searching on online social networks |
US10740375B2 (en) | 2016-01-20 | 2020-08-11 | Facebook, Inc. | Generating answers to questions using information posted by users on online social networks |
US10157224B2 (en) | 2016-02-03 | 2018-12-18 | Facebook, Inc. | Quotations-modules on online social networks |
US10216850B2 (en) | 2016-02-03 | 2019-02-26 | Facebook, Inc. | Sentiment-modules on online social networks |
US10270882B2 (en) | 2016-02-03 | 2019-04-23 | Facebook, Inc. | Mentions-modules on online social networks |
US10242074B2 (en) | 2016-02-03 | 2019-03-26 | Facebook, Inc. | Search-results interfaces for content-item-specific modules on online social networks |
US10452671B2 (en) | 2016-04-26 | 2019-10-22 | Facebook, Inc. | Recommendations from comments on online social networks |
US11531678B2 (en) | 2016-04-26 | 2022-12-20 | Meta Platforms, Inc. | Recommendations from comments on online social networks |
US10430426B2 (en) | 2016-05-03 | 2019-10-01 | International Business Machines Corporation | Response effectiveness determination in a question/answer system |
US10635661B2 (en) | 2016-07-11 | 2020-04-28 | Facebook, Inc. | Keyboard-based corrections for search queries on online social networks |
US10223464B2 (en) | 2016-08-04 | 2019-03-05 | Facebook, Inc. | Suggesting filters for search on online social networks |
US10282483B2 (en) | 2016-08-04 | 2019-05-07 | Facebook, Inc. | Client-side caching of search keywords for online social networks |
US10963463B2 (en) | 2016-08-23 | 2021-03-30 | Scuba Analytics, Inc. | Methods for stratified sampling-based query execution |
US10423387B2 (en) | 2016-08-23 | 2019-09-24 | Interana, Inc. | Methods for highly efficient data sharding |
US10146835B2 (en) | 2016-08-23 | 2018-12-04 | Interana, Inc. | Methods for stratified sampling-based query execution |
US10726022B2 (en) | 2016-08-26 | 2020-07-28 | Facebook, Inc. | Classifying search queries on online social networks |
US10534815B2 (en) | 2016-08-30 | 2020-01-14 | Facebook, Inc. | Customized keyword query suggestions on online social networks |
US10102255B2 (en) | 2016-09-08 | 2018-10-16 | Facebook, Inc. | Categorizing objects for queries on online social networks |
US10645142B2 (en) | 2016-09-20 | 2020-05-05 | Facebook, Inc. | Video keyframes display on online social networks |
US10026021B2 (en) | 2016-09-27 | 2018-07-17 | Facebook, Inc. | Training image-recognition systems using a joint embedding model on online social networks |
US10083379B2 (en) | 2016-09-27 | 2018-09-25 | Facebook, Inc. | Training image-recognition systems based on search queries on online social networks |
US10579688B2 (en) | 2016-10-05 | 2020-03-03 | Facebook, Inc. | Search ranking and recommendations for online social networks based on reconstructed embeddings |
US10311117B2 (en) | 2016-11-18 | 2019-06-04 | Facebook, Inc. | Entity linking to query terms on online social networks |
US10650009B2 (en) | 2016-11-22 | 2020-05-12 | Facebook, Inc. | Generating news headlines on online social networks |
US10313456B2 (en) | 2016-11-30 | 2019-06-04 | Facebook, Inc. | Multi-stage filtering for recommended user connections on online social networks |
US10185763B2 (en) | 2016-11-30 | 2019-01-22 | Facebook, Inc. | Syntactic models for parsing search queries on online social networks |
US10162886B2 (en) | 2016-11-30 | 2018-12-25 | Facebook, Inc. | Embedding-based parsing of search queries on online social networks |
US10235469B2 (en) | 2016-11-30 | 2019-03-19 | Facebook, Inc. | Searching for posts by related entities on online social networks |
US10685047B1 (en) * | 2016-12-08 | 2020-06-16 | Townsend Street Labs, Inc. | Request processing system |
US20220365954A1 (en) * | 2016-12-08 | 2022-11-17 | Okta, Inc. | System for Routing of Requests |
US11468105B1 (en) * | 2016-12-08 | 2022-10-11 | Okta, Inc. | System for routing of requests |
US11928139B2 (en) * | 2016-12-08 | 2024-03-12 | Townsend Street Labs, Inc. | System for routing of requests |
US11223699B1 (en) | 2016-12-21 | 2022-01-11 | Facebook, Inc. | Multiple user recognition with voiceprints on online social networks |
US10607148B1 (en) | 2016-12-21 | 2020-03-31 | Facebook, Inc. | User identification with voiceprints on online social networks |
US10535106B2 (en) | 2016-12-28 | 2020-01-14 | Facebook, Inc. | Selecting user posts related to trending topics on online social networks |
CN106682210A (zh) * | 2016-12-30 | 2017-05-17 | 广州华多网络科技有限公司 | 日志文件查询方法及装置 |
US10713242B2 (en) * | 2017-01-17 | 2020-07-14 | International Business Machines Corporation | Enhancing performance of structured lookups using set operations |
US10489472B2 (en) | 2017-02-13 | 2019-11-26 | Facebook, Inc. | Context-based search suggestions on online social networks |
US10614141B2 (en) | 2017-03-15 | 2020-04-07 | Facebook, Inc. | Vital author snippets on online social networks |
US10769222B2 (en) | 2017-03-20 | 2020-09-08 | Facebook, Inc. | Search result ranking based on post classifiers on online social networks |
US11379861B2 (en) | 2017-05-16 | 2022-07-05 | Meta Platforms, Inc. | Classifying post types on online social networks |
US10552426B2 (en) | 2017-05-23 | 2020-02-04 | International Business Machines Corporation | Adaptive conversational disambiguation system |
US10248645B2 (en) | 2017-05-30 | 2019-04-02 | Facebook, Inc. | Measuring phrase association on online social networks |
US10817483B1 (en) | 2017-05-31 | 2020-10-27 | Townsend Street Labs, Inc. | System for determining and modifying deprecated data entries |
US10268646B2 (en) | 2017-06-06 | 2019-04-23 | Facebook, Inc. | Tensor-based deep relevance model for search on online social networks |
US10901992B2 (en) * | 2017-06-12 | 2021-01-26 | KMS Lighthouse Ltd. | System and method for efficiently handling queries |
US20180357282A1 (en) * | 2017-06-12 | 2018-12-13 | KMS Lighthouse Ltd. | System and method for efficiently handling queries |
US10769138B2 (en) | 2017-06-13 | 2020-09-08 | International Business Machines Corporation | Processing context-based inquiries for knowledge retrieval |
US10489468B2 (en) | 2017-08-22 | 2019-11-26 | Facebook, Inc. | Similarity search using progressive inner products and bounds |
US10776437B2 (en) | 2017-09-12 | 2020-09-15 | Facebook, Inc. | Time-window counters for search results on online social networks |
US10678786B2 (en) | 2017-10-09 | 2020-06-09 | Facebook, Inc. | Translating search queries on online social networks |
US10810214B2 (en) | 2017-11-22 | 2020-10-20 | Facebook, Inc. | Determining related query terms through query-post associations on online social networks |
WO2019100167A1 (en) * | 2017-11-27 | 2019-05-31 | Retailcommon Inc. | Method and system for syntactic searching |
CN107885874A (zh) * | 2017-11-28 | 2018-04-06 | 上海智臻智能网络科技股份有限公司 | 数据查询方法和装置、计算机设备及计算机可读存储介质 |
US11468050B2 (en) | 2017-11-30 | 2022-10-11 | International Business Machines Corporation | Learning user synonyms from sequenced query sessions |
US10963514B2 (en) | 2017-11-30 | 2021-03-30 | Facebook, Inc. | Using related mentions to enhance link probability on online social networks |
CN110019712A (zh) * | 2017-12-07 | 2019-07-16 | 上海智臻智能网络科技股份有限公司 | 多意图查询方法和装置、计算机设备及计算机可读存储介质 |
CN110019714A (zh) * | 2017-12-07 | 2019-07-16 | 上海智臻智能网络科技股份有限公司 | 基于历史结果的多意图查询方法、装置、设备及存储介质 |
CN110019713A (zh) * | 2017-12-07 | 2019-07-16 | 上海智臻智能网络科技股份有限公司 | 基于意图理解的数据检索方法和装置、设备及存储介质 |
US10129705B1 (en) | 2017-12-11 | 2018-11-13 | Facebook, Inc. | Location prediction using wireless signals on online social networks |
US11604968B2 (en) | 2017-12-11 | 2023-03-14 | Meta Platforms, Inc. | Prediction of next place visits on online social networks |
CN108491506A (zh) * | 2018-03-22 | 2018-09-04 | 上海连尚网络科技有限公司 | 用于推送问题答案组合的方法 |
US10795886B1 (en) | 2018-03-30 | 2020-10-06 | Townsend Street Labs, Inc. | Dynamic query routing system |
US10956680B1 (en) * | 2018-10-01 | 2021-03-23 | Knexus Research Corporation | System and method for temporal expressions processing |
US11803556B1 (en) | 2018-12-10 | 2023-10-31 | Townsend Street Labs, Inc. | System for handling workplace queries using online learning to rank |
US10586532B1 (en) * | 2019-01-28 | 2020-03-10 | Babylon Partners Limited | Flexible-response dialogue system through analysis of semantic textual similarity |
CN110032631A (zh) * | 2019-03-26 | 2019-07-19 | 腾讯科技(深圳)有限公司 | 一种信息反馈方法、装置和存储介质 |
US11531707B1 (en) | 2019-09-26 | 2022-12-20 | Okta, Inc. | Personalized search based on account attributes |
US11893385B2 (en) | 2021-02-17 | 2024-02-06 | Open Weaver Inc. | Methods and systems for automated software natural language documentation |
WO2022178517A1 (en) * | 2021-02-17 | 2022-08-25 | Iqvia, Inc. | Skipping natural language processor |
US11836069B2 (en) | 2021-02-24 | 2023-12-05 | Open Weaver Inc. | Methods and systems for assessing functional validation of software components comparing source code and feature documentation |
US11836202B2 (en) | 2021-02-24 | 2023-12-05 | Open Weaver Inc. | Methods and systems for dynamic search listing ranking of software components |
US11921763B2 (en) | 2021-02-24 | 2024-03-05 | Open Weaver Inc. | Methods and systems to parse a software component search query to enable multi entity search |
US11947530B2 (en) | 2021-02-24 | 2024-04-02 | Open Weaver Inc. | Methods and systems to automatically generate search queries from software documents to validate software component search engines |
US11853745B2 (en) | 2021-02-26 | 2023-12-26 | Open Weaver Inc. | Methods and systems for automated open source software reuse scoring |
WO2022187495A3 (en) * | 2021-03-04 | 2022-10-27 | Yext, Inc. | Search experience management system |
US11954157B2 (en) | 2021-07-23 | 2024-04-09 | Veveo, Inc. | Method of and system for conducting personalized federated search and presentation of results therefrom |
US11960492B2 (en) | 2022-02-23 | 2024-04-16 | Open Weaver Inc. | Methods and systems for display of search item scores and related information for easier search result selection |
Also Published As
Publication number | Publication date |
---|---|
US6766320B1 (en) | 2004-07-20 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US6766320B1 (en) | Search engine with natural language-based robust parsing for user query and relevance feedback learning | |
US7089236B1 (en) | Search engine interface | |
US8903810B2 (en) | Techniques for ranking search results | |
US8812541B2 (en) | Generation of refinement terms for search queries | |
US7702677B2 (en) | Information retrieval from a collection of data | |
US8645405B2 (en) | Natural language expression in response to a query | |
US7756855B2 (en) | Search phrase refinement by search term replacement | |
US6714905B1 (en) | Parsing ambiguous grammar | |
US7406459B2 (en) | Concept network | |
US6745181B1 (en) | Information access method | |
US20050060290A1 (en) | Automatic query routing and rank configuration for search queries in an information retrieval system | |
US20040167875A1 (en) | Information processing method and system | |
US20070022099A1 (en) | Question answering system, data search method, and computer program | |
US20060161543A1 (en) | Systems and methods for providing search results based on linguistic analysis | |
US20030004932A1 (en) | Method and system for knowledge repository exploration and visualization | |
KR20050032937A (ko) | 언어분석 기반 자동 질문/정답 색인 방법과 그 질의응답방법 및 시스템 | |
WO2009152469A1 (en) | Systems and methods for classifying search queries | |
KR102285232B1 (ko) | 형태소 기반 ai 챗봇 및 그의 문장의도 결정 방법 | |
WO2007124430A2 (en) | Search techniques using association graphs | |
US8640017B1 (en) | Bootstrapping in information access systems | |
US7127450B1 (en) | Intelligent discard in information access system | |
JP2006529044A (ja) | 定義付けシステムおよび方法 | |
US20060059126A1 (en) | System and method for network searching | |
Mittal et al. | A fully automatic question-answering system for intelligent search in e-learning documents | |
Pizzato et al. | Extracting exact answers using a meta question answering system |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |
|
AS | Assignment |
Owner name: MICROSOFT TECHNOLOGY LICENSING, LLC, WASHINGTON Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:MICROSOFT CORPORATION;REEL/FRAME:034766/0001 Effective date: 20141014 |