CN111984774A - Search method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a searching method, a searching device, searching equipment and a storage medium, and relates to big data technologies in the field of artificial intelligence, in particular to technologies such as intelligent searching and intelligent recommendation. The specific implementation scheme is as follows: determining candidate questions from knowledge questions associated with question-and-answer knowledge according to the search term questions in the target search term; determining a target question from the candidate questions according to the search term question based on question sentence type information and/or sentence component information; and taking the question-answer knowledge associated with the target question as a search result of the target search term. The techniques according to the present application improve the accuracy of matching search term problems with knowledge problems.
Description
Technical Field
The application relates to big data technology in the field of artificial intelligence, in particular to technologies such as intelligent search and intelligent recommendation, and specifically relates to a search method, a search device, search equipment and a storage medium.
Background
With the rapid development of network technology, network search engines have become an important means for people to obtain information, and users obtain search results returned by search engines by inputting search terms (query) in the search engines and find information needed by themselves.
Along with the gradual enrichment of the knowledge question-answer resources, in order to improve the richness of the search results, the search results of the query need to be determined from the knowledge question-answer resources. However, because the question expression in the query is various, it is difficult for the search engine to match the answer satisfied by the user from the knowledge question-answer resources based on the query.
Disclosure of Invention
The disclosure provides a search method, apparatus, device and storage medium.
According to an aspect of the present disclosure, there is provided a search method including:
determining candidate questions from knowledge questions associated with question-and-answer knowledge according to the search term questions in the target search term;
determining a target question from the candidate questions according to the search term question based on question sentence type information and/or sentence component information;
and taking the question-answer knowledge associated with the target question as a search result of the target search term.
According to another aspect of the present disclosure, there is provided an apparatus for searching, including:
the candidate question determining module is used for determining candidate questions from knowledge questions related to question-answer knowledge according to the search term questions in the target search terms;
a target question determining module for determining a target question from the candidate questions according to the search term question based on question sentence type information and/or sentence component information;
and the search result determining module is used for taking the question-answer knowledge related to the target question as the search result of the target search term.
According to still another aspect of the present disclosure, there is provided an electronic apparatus, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the embodiments of the present application.
According to yet another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of the embodiments of the present application.
The techniques according to the present application improve the accuracy of matching search term problems with knowledge problems.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
fig. 1 is a flowchart of a search method provided in an embodiment of the present application;
FIG. 2 is a flow chart of another searching method provided by the embodiment of the application;
FIG. 3 is a flow chart of another searching method provided by the embodiment of the present application;
FIG. 4 is a flowchart of another searching method provided by an embodiment of the present application;
FIG. 5 is a flowchart of another searching method provided by an embodiment of the present application;
FIG. 6 is a schematic diagram of a search apparatus according to an embodiment of the present disclosure;
fig. 7 is a block diagram of an electronic device of a search method according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a flowchart of a search method provided in an embodiment of the present application. The method and the device for determining the search results of the user can be applied to the situation that the search results of the user are determined from the question and answer knowledge. The method may be performed by a search method apparatus, which may be implemented by software and/or hardware. Referring to fig. 1, a search method provided in an embodiment of the present application includes:
s110, determining candidate questions from knowledge questions related to question-answer knowledge according to the search term questions in the target search terms.
Wherein the target search term refers to a search term to be searched. Typically, the target search term refers to a search question entered by the user.
The search term question refers to a question that the target search term includes, i.e., a question that the user wants to search.
For example, the search term question may be what weather is tomorrow, which certificates are required for the examination of the driver's license, and the like.
The question-answer knowledge refers to answer information aiming at questions on the knowledge question-answer sharing platform.
The knowledge question refers to a question corresponding to question-answer knowledge.
An alternative problem refers to a problem that may be descriptive of the actual needs of the user in the targeted search term.
And S120, determining a target question from the candidate questions according to the search term questions based on question sentence type information and/or sentence component information.
The question type information is information describing the question type to which the question belongs.
In one embodiment, the question type information may include: at least one of a non-question, a special question, a selective question, a positive and negative question, and a negative question.
The sentence component information refers to sentence component information describing a question related sentence.
In one embodiment, the sentence component information may include: an entity, a verb, a modifier, an appendage, an active relationship, and a passive relationship.
A target question refers to a question that can describe the actual needs of a user in a target search term.
S130, using the question-answer knowledge associated with the target question as a search result of the target search term.
According to the technical scheme of the embodiment of the application, the target question is determined from the candidate questions according to the search term question based on the question sentence type information and/or the sentence component information, and question-answer knowledge related to the target question is used as the search result of the target search term, so that the search result is determined from the question-answer knowledge. In addition, matching is performed based on information of multiple dimensions, so that the matching accuracy of the search term problem and the candidate problem can be improved, and the accuracy of the search result is improved.
Fig. 2 is a flowchart of another search method provided in an embodiment of the present application. The present embodiment is a specific optimization of the above step "determining a target question from the candidate questions according to the search term question based on question sentence type information and/or sentence component information" on the basis of the above embodiment. Referring to fig. 2, a search method provided in an embodiment of the present application includes:
s210, determining candidate questions from knowledge questions related to question-answer knowledge according to the search term questions in the target search terms.
S220, matching the question type information of the search term question with the question type information of the candidate question, and/or matching the sentence component information of the search term question with the sentence component information of the candidate question.
And S230, determining the target problem from the candidate problems according to the matching result.
Alternatively, the matching result may include a matching result of the question type information of the search term question and the question type information of the candidate question.
The matching result may also include a matching result of sentence component information of the search term question and sentence component information of the candidate question.
The matching result may further include a matching result of the question type information of the search term question and the question type information of the candidate question, and a matching result of the sentence component information of the search term question and the sentence component information of the candidate question.
S240, using the question-answer knowledge associated with the target question as a search result of the target search term.
The method matches the question sentence type information of the search term problem with the question sentence type information of the candidate problem, and/or matches the sentence component information of the search term problem with the sentence component information of the candidate problem; and determining the target problem from the candidate problems according to the matching result, thereby realizing the determination of the target problem.
Fig. 3 is a flowchart of another search method provided in an embodiment of the present application. The present embodiment is a specific optimization of the above-mentioned step of "determining candidate questions from knowledge questions related to question-and-answer knowledge based on the search term questions in the target search term" on the basis of the above-mentioned embodiment. Referring to fig. 3, a search method provided in an embodiment of the present application includes:
s310, calculating the similarity between the search term questions in the target search term and the knowledge questions related to the question-answer knowledge.
In one embodiment, the similarity between the search term problem and the knowledge problem can be calculated by using the set similarity calculation logic.
Typically, the similarity between the search term problem and the knowledge problem can also be calculated by utilizing a pre-trained semantic similarity calculation model.
S320, determining the candidate problem from the knowledge problem according to the calculated similarity.
Where the similarity is a similarity between the search term problem and the knowledge problem.
In order to further improve the accuracy of the search result of the target search term, the candidate problem is determined from the knowledge problem according to the calculated similarity, and the method comprises the following steps:
and taking the knowledge questions with the similarity greater than the set similarity threshold as candidate questions to further extract the search results of the target search terms from question-answer knowledge associated with the knowledge questions with high similarity.
S330, determining a target question from the candidate questions according to the search term questions based on question sentence type information and/or sentence component information.
S340, using the question-answer knowledge associated with the target question as a search result of the target search term.
According to the method and the device, the candidate problem is determined from the knowledge problem according to the similarity between the search term problem and the knowledge problem obtained through calculation, and therefore the candidate problem is determined.
In order to improve the recall rate of the search result of the target search term, the determining the candidate question from the knowledge question according to the calculated similarity comprises:
if the calculated similarity is smaller than a set similarity threshold, determining the knowledge problem as the candidate problem;
and if the calculated similarity is greater than or equal to the set similarity threshold, taking the knowledge problem as a target problem.
Based on the technical characteristics, the search results of the target search term are extracted from the question-answer knowledge associated with the knowledge problem with low similarity, so that the recall rate of the search results of the target search term is improved.
Fig. 4 is a flowchart of another search method provided in an embodiment of the present application. The embodiment is a further extension of the above scheme on the basis of the above embodiment. Referring to fig. 4, a search method provided in an embodiment of the present application includes:
s410, identifying the sentence pattern of the search term question in the target search term and the sentence pattern of the knowledge question related to the question-answer knowledge.
Herein, a sentence pattern refers to a type of a sentence. In one embodiment, the sentence pattern includes question sentences, statement sentences, exclamation sentences, and the like.
And S420, if the search term problem and the knowledge problem are both question sentences, determining candidate problems from the knowledge problems according to the search term problem.
S430, based on the question sentence type information and/or sentence component information, according to the search term question, determining a target question from the candidate questions.
S440, using the question-answer knowledge associated with the target question as a search result of the target search term.
According to the scheme, the problem that the question sentence is not filtered is eliminated, so that the matching accuracy of the search term problem and the knowledge problem is improved, and the accuracy of the search result is further improved.
Fig. 5 is a flowchart of another search method provided in an embodiment of the present application. The present embodiment is an alternative provided on the basis of the above-described embodiments. Referring to fig. 5, a search method provided in an embodiment of the present application includes:
performing question semantic analysis on the search term problems in the target search term and knowledge problems related to question-answer knowledge;
if the question is not the answer content, filtering out; otherwise, based on the deep learning model, semantic similarity calculation is carried out on the search term problems in the target search term and the knowledge problems related to the question-answer knowledge;
sorting the knowledge problems according to the calculation result, taking question-answer knowledge associated with the knowledge problems with higher similarity as the search result of the target search term according to the sorting result, and taking other problems in the knowledge problems as candidate problems;
performing sentence pattern type analysis on the search term problem and the candidate problem based on the deep learning model;
if the sentence pattern types of the search term problem and the candidate problem are the same, respectively making a sub-analysis on the search term problem and the knowledge problem by utilizing a natural language processing basic model, extracting corresponding entities, core verbs, modifiers, auxiliary words, active relations, passive relations and the like, and respectively matching in each dimension;
and taking the question-answer knowledge corresponding to the successfully matched candidate question as a search result of the target search item.
And in the entity matching, if the comparison result shows that entity nouns contained in the search term problem and the knowledge problem are completely consistent or have the same semantics, the matching is successful, otherwise, the matching is failed.
Core verb matching and modifier matching are similar to entity matching.
The distinction between search term problems and knowledge problems can be achieved based on the matching of active and passive relationships. For example: the search term problem is: how to locate the apple phone with the android phone? The knowledge problem is that: how to locate the android phone with the apple phone? Because the word order is different, the semantics of the representation are also different. Matching based on active and passive relationships can solve this type of matching problem.
According to the scheme, non-question-answer data are removed through question recognition and analysis; then calculating the similarity between the search term problem and the knowledge problem to determine a candidate problem; performing multidimensional syntactic analysis on the search term problem and the candidate problem, and respectively matching in each dimension; and determining a target question from the candidate questions according to the matching result, and taking question-answer knowledge corresponding to the target question as a search result of the target search item, thereby realizing high-accuracy matching of the search item question and the knowledge question.
Fig. 6 is a schematic diagram of a search apparatus according to an embodiment of the present application. Referring to fig. 6, a search apparatus 600 provided in the embodiment of the present application includes: a candidate problem determination module 601, a target problem determination module 602, and a search result determination module 603.
The candidate question determining module 601 is configured to determine a candidate question from knowledge questions related to question-and-answer knowledge according to a search term question in a target search term;
a target question determining module 602, configured to determine a target question from the candidate questions according to the search term question based on question sentence type information and/or sentence component information;
a search result determining module 603, configured to use the question-answer knowledge associated with the target question as a search result of the target search term.
According to the technical scheme of the embodiment of the application, the target question is determined from the candidate questions according to the search term question based on the question sentence type information and/or the sentence component information, and question-answer knowledge related to the target question is used as the search result of the target search term, so that the search result is determined from the question-answer knowledge. And because the matching accuracy of the search term problem and the candidate problem can be realized based on the question sentence type information and/or the sentence component information, the accuracy of the search result can be improved.
Further, the target issue determination module includes:
an information matching unit for matching the question type information of the search term question with the question type information of the candidate question and/or matching the sentence component information of the search term question with the sentence component information of the candidate question;
and the matching result unit is used for determining the target problem from the candidate problems according to the matching result.
Further, the question type information includes: at least one of a non-question, a special question, a selective question, a positive and negative question and a negative question;
the sentence component information includes: an entity, a verb, a modifier, an appendage, an active relationship, and a passive relationship.
Further, the candidate problem determination module includes:
a similarity calculation unit for calculating a similarity between the search term problem and the knowledge problem;
a candidate problem determining unit, configured to determine the candidate problem from the knowledge problem according to the calculated similarity;
further, the candidate problem determination unit is specifically configured to:
and if the calculated similarity is smaller than a set similarity threshold, determining the knowledge problem as the candidate problem.
Further, the apparatus further comprises:
a sentence pattern recognition module for recognizing the sentence pattern of the search term question and the sentence pattern of the knowledge question before determining candidate questions from the knowledge questions related to the question-answer knowledge according to the search term question in the target search term;
and the trigger execution module is used for triggering and executing the step of determining candidate questions from knowledge questions related to question-answer knowledge according to the search term questions in the target search term if the search term questions and the knowledge questions are both question sentences.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 7 is a block diagram of an electronic device according to a searching method of the embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 7, the electronic apparatus includes: one or more processors 701, a memory 702, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 7, one processor 701 is taken as an example.
The memory 702 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform a method of searching as provided herein. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform a method of searching provided herein.
The memory 702, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to a method of searching in embodiments of the present application (e.g., the candidate problem determination module 601, the target problem determination module 602, and the search result determination module 603 shown in fig. 6). The processor 701 executes various functional applications of the server and data processing, i.e., a method of implementing a search in the above-described method embodiments, by executing non-transitory software programs, instructions, and modules stored in the memory 702.
The memory 702 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of an electronic device for a kind of search, and the like. Further, the memory 702 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 702 may optionally include memory located remotely from processor 701, which may be connected to a searching electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of a method of searching may further include: an input device 703 and an output device 704. The processor 701, the memory 702, the input device 703 and the output device 704 may be connected by a bus or other means, and fig. 7 illustrates an example of a connection by a bus.
The input device 703 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of a searched electronic apparatus, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or other input device. The output devices 704 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
According to the technology of the application, a short text matching scheme based on semantic understanding is realized, the mapping relation between the search and the content title is established, and the semantic matching accuracy is improved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (14)
1. A search method, comprising:
determining candidate questions from knowledge questions associated with question-and-answer knowledge according to the search term questions in the target search term;
determining a target question from the candidate questions according to the search term question based on question sentence type information and/or sentence component information;
and taking the question-answer knowledge associated with the target question as a search result of the target search term.
2. The method of claim 1, wherein the determining a target question from the candidate questions according to the search term question based on question type information and/or sentence component information comprises:
matching question type information of the search term question with question type information of the candidate question, and/or matching sentence component information of the search term question with sentence component information of the candidate question;
and determining the target problem from the candidate problems according to the matching result.
3. The method of claim 2, wherein the question type information comprises: at least one of a non-question, a special question, a selective question, a positive and negative question and a negative question;
the sentence component information includes: an entity, a verb, a modifier, an appendage, an active relationship, and a passive relationship.
4. The method according to any one of claims 1-3, wherein the determining candidate questions from knowledge questions related to question-answer knowledge based on search term questions in the target search term comprises:
calculating a similarity between the search term problem and the knowledge problem;
and determining the candidate problem from the knowledge problem according to the calculated similarity.
5. The method of claim 4, wherein said determining the candidate questions from the knowledge questions based on the calculated similarities comprises:
and if the calculated similarity is smaller than a set similarity threshold, determining the knowledge problem as the candidate problem.
6. The method according to any one of claims 1-3, wherein before determining candidate questions from knowledge questions related to question-and-answer knowledge based on search term questions in the target search term, the method further comprises:
identifying a sentence pattern of the search term problem and a sentence pattern of the knowledge problem;
and if the search term question and the knowledge question are both question sentences, triggering and executing the step of determining candidate questions from the knowledge questions related to question-answer knowledge according to the search term question in the target search term.
7. An apparatus for searching, comprising:
the candidate question determining module is used for determining candidate questions from knowledge questions related to question-answer knowledge according to the search term questions in the target search terms;
a target question determining module for determining a target question from the candidate questions according to the search term question based on question sentence type information and/or sentence component information;
and the search result determining module is used for taking the question-answer knowledge related to the target question as the search result of the target search term.
8. The apparatus of claim 7, wherein the target issue determination module comprises:
an information matching unit for matching the question type information of the search term question with the question type information of the candidate question and/or matching the sentence component information of the search term question with the sentence component information of the candidate question;
and the matching result unit is used for determining the target problem from the candidate problems according to the matching result.
9. The apparatus of claim 8, wherein the question type information comprises: at least one of a non-question, a special question, a selective question, a positive and negative question and a negative question;
the sentence component information includes: an entity, a verb, a modifier, an appendage, an active relationship, and a passive relationship.
10. The apparatus of any of claims 7-9, wherein the candidate problem determination module comprises:
a similarity calculation unit for calculating a similarity between the search term problem and the knowledge problem;
and the candidate problem determining unit is used for determining the candidate problem from the knowledge problem according to the calculated similarity.
11. The apparatus according to claim 10, wherein the candidate problem determination unit is specifically configured to:
and if the calculated similarity is smaller than a set similarity threshold, determining the knowledge problem as the candidate problem.
12. The apparatus according to any one of claims 7-9, the apparatus further comprising:
a sentence pattern recognition module for recognizing the sentence pattern of the search term question and the sentence pattern of the knowledge question before determining candidate questions from the knowledge questions related to the question-answer knowledge according to the search term question in the target search term;
and the trigger execution module is used for triggering and executing the step of determining candidate questions from knowledge questions related to question-answer knowledge according to the search term questions in the target search term if the search term questions and the knowledge questions are both question sentences.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.
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