CN113792083A - Event extraction and judgment method and system - Google Patents
Event extraction and judgment method and system Download PDFInfo
- Publication number
- CN113792083A CN113792083A CN202110612736.4A CN202110612736A CN113792083A CN 113792083 A CN113792083 A CN 113792083A CN 202110612736 A CN202110612736 A CN 202110612736A CN 113792083 A CN113792083 A CN 113792083A
- Authority
- CN
- China
- Prior art keywords
- target
- event
- corpus
- extraction
- type
- 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.)
- Granted
Links
- 238000000605 extraction Methods 0.000 title claims abstract description 54
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 52
- 238000007781 pre-processing Methods 0.000 claims abstract description 16
- 230000015654 memory Effects 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 15
- 239000013598 vector Substances 0.000 claims description 15
- 238000012545 processing Methods 0.000 claims description 10
- 238000007635 classification algorithm Methods 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 4
- 230000011218 segmentation Effects 0.000 claims description 4
- 235000007516 Chrysanthemum Nutrition 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 3
- 238000012986 modification Methods 0.000 claims description 3
- 230000004048 modification Effects 0.000 claims description 3
- 244000189548 Chrysanthemum x morifolium Species 0.000 claims 1
- 230000010365 information processing Effects 0.000 abstract description 2
- 238000012216 screening Methods 0.000 abstract 1
- 238000004891 communication Methods 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 241000723353 Chrysanthemum Species 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000003058 natural language processing Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012567 pattern recognition method Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
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
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- 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
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
- G06F40/211—Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
- G06F40/295—Named entity recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Audiology, Speech & Language Pathology (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Machine Translation (AREA)
Abstract
The invention discloses an event extraction judgment method and system, relating to the field of information processing and having the technical key points that the method comprises the following steps: acquiring a natural corpus, and preprocessing the natural corpus to acquire a target corpus; based on the target corpus, respectively judging the event types of the target corpus by using an algorithm library so as to respectively obtain a plurality of groups of target type results; outputting an optimal type result based on the multiple groups of target type results; extracting target event elements of the target corpus based on named entity recognition and pattern matching algorithm, and distinguishing the target event elements; and outputting an event extraction result based on the corresponding relation between the optimal type result and the target event element. The event type extraction has higher precision through the algorithm library. Meanwhile, the accuracy of event element extraction is improved through the two-stage recall fine screening, and the algorithm precision of the whole event extraction task in a specific service scene is improved.
Description
Technical Field
The invention relates to the field of information processing, in particular to an event extraction and judgment method and system.
Background
With the rapid development of the internet, more and more information is presented to users in the form of electronic text. In order to help a user to quickly find needed information in massive information, an information extraction concept is provided. Information extraction refers to extracting factual information from natural language text and describing the information in a structured form. The event extraction is an important research direction in information extraction, and mainly refers to extracting interesting event information from text data containing the event information, and presenting the events expressed by natural language in a structured form, such as people, places, time and things, so that the event extraction has a very wide application prospect in the present massive information era.
The event extraction is to extract the events which are interested by the user from the text describing the event information and present the events in a structured form, so that the events can be extracted from massive natural texts, and chat software in the current market is more, such as QQ and WeChat which are commonly used in daily communication; stings of long duration in work. Chat software is an indispensable network tool for people at present, regardless of work or study.
Natural language is commonly adopted among users of chat software as a communication means, and the natural language of chat often contains a lot of event information. The event referred to herein specifically includes two attributes of an event type and an event element, wherein the event element is different in content according to different event types, but generally includes fields of time, place, person, and the like.
At present, the event extraction technology for natural language in the chat software usually involves two aspects, namely judgment of event types and extraction of event elements. The existing event extraction methods include pattern matching based and machine learning based methods. The pattern matching in the professional field needs to define a large number of templates for event recognition and extraction; traditional machine learning-based approaches typically convert event extraction problems into classification problems, based on phrase or sentence-level information. The method comprises the steps of performing sentence segmentation, word segmentation, entity identification, syntax and dependency relationship on text information, extracting word meaning characteristics and semantic characteristics of contexts of candidate words by using a natural language processing tool, constructing a characteristic vector as input of a classifier, predicting a trigger word of an event by using the classifier, and judging the type of the event according to the type of the trigger word.
The event extraction method based on pattern matching matches sentences to be extracted with an existing template based on a certain pattern (context environment), wherein the construction of the pattern requires expert knowledge in the field, manual establishment is carried out, the labor and time costs are high, the portability is poor, and the transplanting from one field to another field is equivalent to reestablishing; the event extraction method based on machine learning comprises the steps of dividing the event extraction into an entity extraction stage and an event judgment stage, wherein the judgment of an event is influenced by the error of named entity identification, and an accumulated error is brought; secondly, for a specific field, a large number of artificial features are often required to be constructed, the cost of the feature selection process is high, and the maintainability becomes worse and worse as the complexity of the model is improved.
Disclosure of Invention
The invention aims to provide an event extraction judgment method and system, which at least solve the problem that certain false detection rate is often caused in event extraction due to the characteristics of poor flexibility and complex form of a template.
In order to achieve the purpose, the invention provides the following technical scheme: an event extraction judging method comprises the following steps:
acquiring a natural corpus, and preprocessing the natural corpus to acquire a target corpus;
based on the target corpus, respectively judging the event types of the target corpus by using an algorithm library so as to respectively obtain a plurality of groups of target type results;
outputting an optimal type result based on the multiple groups of target type results;
extracting target event elements of the target corpus based on named entity recognition and pattern matching algorithm, and distinguishing the target event elements;
and outputting an event extraction result based on the corresponding relation between the optimal type result and the target event element.
The application is further configured to pre-process the natural corpus to obtain a target corpus,
the method comprises the steps of performing semantic error correction, sentence segmentation processing and uncommon word processing on the natural corpus.
The application is further configured to, based on the target corpus, respectively perform event type judgment on the target corpus by using an algorithm library to respectively obtain a plurality of groups of target type results, wherein the algorithm library at least comprises a text classification algorithm, a text matching algorithm and a trigger word matching algorithm;
performing event type classification on the target corpus by a text classification algorithm;
the text matching algorithm is constructed by constructing a sample vector library, collecting strong intention information in sample corpus data by collecting sample corpus data, and classifying event types by vectorizing a target corpus, collecting target strong intention information corpus and distance between the target strong intention information corpus and a vector autumn chrysanthemum in the vector library;
the trigger matching algorithm classifies event types through trigger and trigger modes.
The application is further configured such that the target event elements of the target corpus are extracted based on the named entity recognition and pattern matching algorithm, wherein the recognition result based on the named entity recognition is input to a template dictionary of the pattern matching algorithm for correction and filtering.
The application also provides an event extraction judgment system which is characterized by comprising a data preprocessing module, an event type judgment module, an event element extraction module and an event output module;
the data preprocessing module is configured to acquire natural corpus and preprocess the natural corpus to acquire target corpus;
the event type judging module is configured to respectively judge the event types of the target linguistic data by using an algorithm library based on the target linguistic data so as to respectively obtain a plurality of groups of target type results, and output an optimal type result based on the plurality of groups of target type results;
the event element extraction module is configured to extract target event elements of the target corpus and judge the target event elements based on named entity recognition and pattern matching algorithm;
the event output module is configured to output an event extraction result based on the correspondence between the optimal type result and the target event element.
The application is further configured to further include a data collection module configured to collect the natural corpus through microphone or keyboard input and send the natural corpus to the data preprocessing module.
The present application also provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to perform the aforementioned method when executed.
The present application also provides an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the aforementioned method.
Compared with the prior art, the invention has the following beneficial effects: different improvements are carried out at each stage of natural corpus event extraction; and the method integrates multiple natural language processing technologies such as text disambiguation, dependency syntax analysis, named entity identification, text semantic matching, text classification and the like. The whole event mining process can be more flexible and intelligent, and the manual workload is reduced; and the method further adopts a template and dictionary mode to control and bottom-holding the event extraction process and result, so as to ensure the controllability and accuracy of the event extraction result.
Drawings
Fig. 1 is a block diagram of a hardware structure of a mobile terminal according to an event extraction determination method in an embodiment of the present application;
FIG. 2 is a flow chart of the general steps of the method of the present application;
FIG. 3 is a flowchart illustrating the detailed steps of the extraction and determination method according to the present application;
fig. 4 is a block diagram of the present application.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking an example of an event running on a mobile terminal, fig. 1 is a hardware structure block diagram of the mobile terminal of an event extraction and determination method according to an embodiment of the present application. As shown in fig. 1, the mobile terminal may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data, wherein the mobile terminal may further include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 can be used for storing a computer program, for example, a software program and a module of an application software, such as a computer program corresponding to a method for measuring a distribution parameter of a high-frequency transformer in the embodiment of the present application, and the processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, so as to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal over 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 transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the internet via wireless.
As shown in fig. 2 and 3, an event extraction and determination method includes the following steps:
acquiring natural corpus, and preprocessing the natural corpus to acquire target corpus, wherein semantic error correction, sentence breaking processing and uncommon word processing are performed on the natural corpus;
based on the target corpus, respectively judging the event types of the target corpus by using an algorithm library so as to respectively obtain a plurality of groups of target type results;
outputting an optimal type result based on the multiple groups of target type results;
extracting target event elements of the target corpus based on named entity recognition and pattern matching algorithm, and distinguishing the target event elements;
and outputting an event extraction result based on the corresponding relation between the optimal type result and the target event element.
The natural corpus is preprocessed, chat natural language information obtained by chat software or other communication environments is used as input in the preprocessing, the input natural language data is used as the natural corpus, and the input natural language data needs to be processed by a pycorrector semantic error correction tool because the chat sentences are large and the situations of wrongly written or ill-formed words can exist. The sentence breaking processing is needed for longer natural language, and the processing and replacement are needed for partial special characters and stop words so as to ensure the extraction quality of the follow-up events.
And based on the target linguistic data, respectively judging the event types of the target linguistic data by using an algorithm library so as to respectively obtain a plurality of groups of target type results. Wherein; the algorithm library at least comprises a text classification algorithm, a text matching algorithm and a trigger word matching algorithm, the three algorithms in the algorithm library are adopted for simultaneous event type judgment, and in practical implementation, the three algorithms are not limited to be used, but the idea of simultaneous calculation by adopting multiple algorithms is still in the central idea of the embodiment.
Specifically, in the first embodiment, through a text classification algorithm, the event type classification can be performed on the target corpus by using a natural language classification algorithm of albert + nn.
Secondly, a text matching algorithm is constructed by constructing a sample vector library, the sample vector library is constructed by collecting sample corpus data and collecting strong intention information in the sample corpus data, and the text matching algorithm is constructed by vectorizing a target corpus, collecting target strong intention information corpus and carrying out event type classification by the distance between the target strong intention information corpus and a vector autumn chrysanthemum in the vector library; the method comprises the steps of vectorizing chat information by using a simbert + write method, storing collected information vectors with strong intentions to form a vector library to be searched, and solving the distance between new chat natural language information and vectors in the vector library so as to judge the event type of the text.
And thirdly, the trigger word matching algorithm classifies the event types through the trigger words and the trigger modes. The chat text is matched by adopting a traditional method of triggering words and triggering modes, and the event type of the chat text is judged.
After the three algorithms are used for calculating three groups of event calculation results, the optimal type result is output based on the multiple groups of target type results, in the embodiment, the tree model is adopted to synthesize the results of the three algorithms, the optimal event type judgment result is output, and other neural network models or traditional model algorithms can be used for judging and selecting one for output after sample training or manual parameter input.
And extracting target event elements of the target corpus based on the named entity recognition and pattern matching algorithm, wherein the recognition result based on the named entity recognition is input into a template dictionary of the pattern matching algorithm for correction and filtration. The event elements are distinguished and mined by using two algorithms of named entity recognition and pattern matching. An albert + self-attack model is adopted for named entity recognition on a chat natural language, and unstable characteristics exist in a model recognition result under partial conditions. The ner results are put into a template dictionary for correction and filtering. In this module, the ner's method acts as a broad recall event element, while the template dictionary acts as a further fine screen.
Due to the comprehensive use of a text classification algorithm, a semantic matching algorithm and a trigger word matching method, the method has higher precision in event type extraction. Meanwhile, a ner algorithm and a pattern recognition method are combined, and the accuracy of event element extraction is improved through a two-stage recall fine screen. The embodiment improves the algorithm precision of the event extraction task under a specific service scene as a whole.
As shown in fig. 3 and fig. 4, the present embodiment further discloses an event extraction and judgment system, which applies the foregoing method and includes a data preprocessing module, an event type judgment module, an event element extraction module, and an event output module;
the data preprocessing module is configured to acquire natural corpus and preprocess the natural corpus to acquire target corpus;
the event type judging module is configured to respectively judge the event types of the target linguistic data by using an algorithm library based on the target linguistic data so as to respectively obtain a plurality of groups of target type results, and output an optimal type result based on the plurality of groups of target type results;
the event element extraction module is configured to extract target event elements of the target corpus and judge the target event elements based on named entity recognition and pattern matching algorithm;
the event output module is configured to output an event extraction result based on the correspondence between the optimal type result and the target event element.
The system comprises a data preprocessing module and a data acquisition module, wherein the data acquisition module is configured to collect natural corpora through microphone or keyboard input and send the natural corpora to the data preprocessing module.
The present application also provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the aforementioned method when executed.
The present application also provides an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the aforementioned method.
Alternatively, in this embodiment, a person skilled in the art may understand that all or part of the steps in the methods of the foregoing embodiments may be implemented by instructing hardware associated with the terminal device through a program, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on this understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing one or more computer devices (which may be personal computers, servers, network devices, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed client can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other ways of dividing the actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a hardware form, and can also be realized in a software functional unit form.
The foregoing is only a preferred embodiment of the present application and it should be noted that, for a person skilled in the art, several modifications and improvements can be made without departing from the principle of the present application, and these modifications and improvements should also be considered as the protection scope of the present application.
Claims (8)
1. An event extraction and judgment method is characterized by comprising the following steps:
acquiring a natural corpus, and preprocessing the natural corpus to acquire a target corpus;
based on the target corpus, respectively judging the event types of the target corpus by using an algorithm library so as to respectively obtain a plurality of groups of target type results;
outputting an optimal type result based on the multiple groups of target type results;
extracting target event elements of the target corpus based on named entity recognition and pattern matching algorithm, and distinguishing the target event elements;
and outputting an event extraction result based on the corresponding relation between the optimal type result and the target event element.
2. The method as claimed in claim 1, wherein the pre-processing of the natural corpus to obtain the target corpus comprises semantic error correction, sentence segmentation and word processing.
3. The method according to claim 1, wherein the event type is determined by using an algorithm library based on the target corpus to obtain a plurality of target type results, wherein the algorithm library at least comprises a text classification algorithm, a text matching algorithm and a trigger matching algorithm;
performing event type classification on the target corpus by a text classification algorithm;
the text matching algorithm is constructed by constructing a sample vector library, collecting strong intention information in sample corpus data by collecting sample corpus data, and classifying event types by vectorizing a target corpus, collecting target strong intention information corpus and distance between the target strong intention information corpus and a vector autumn chrysanthemum in the vector library;
the trigger matching algorithm classifies event types through trigger and trigger modes.
4. The method as claimed in claim 1, wherein the target event elements of the target corpus are extracted based on the named entity recognition and pattern matching algorithm, and wherein the recognition result based on the named entity recognition is inputted into the template dictionary of the pattern matching algorithm for modification and filtering.
5. An event extraction judging system is characterized by comprising a data preprocessing module, an event type judging module, an event element extraction module and an event output module;
the data preprocessing module is configured to acquire natural corpus and preprocess the natural corpus to acquire target corpus;
the event type judging module is configured to respectively judge the event types of the target linguistic data by using an algorithm library based on the target linguistic data so as to respectively obtain a plurality of groups of target type results, and output an optimal type result based on the plurality of groups of target type results;
the event element extraction module is configured to extract target event elements of the target corpus based on named entity recognition and pattern matching algorithm, and judge the target event elements;
the event output module is configured to output an event extraction result based on the corresponding relationship between the optimal type result and the target event element.
6. The event extraction and judgment system according to claim 5, further comprising a data collection module, wherein the data collection module is configured to collect natural corpus by microphone or keyboard input and send the natural corpus to the data preprocessing module.
7. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to carry out the method of any one of claims 1 to 4 when executed.
8. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110612736.4A CN113792083B (en) | 2021-06-02 | 2021-06-02 | Event extraction judging method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110612736.4A CN113792083B (en) | 2021-06-02 | 2021-06-02 | Event extraction judging method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113792083A true CN113792083A (en) | 2021-12-14 |
| CN113792083B CN113792083B (en) | 2024-06-18 |
Family
ID=79181257
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110612736.4A Active CN113792083B (en) | 2021-06-02 | 2021-06-02 | Event extraction judging method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113792083B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104572958A (en) * | 2014-12-29 | 2015-04-29 | 中国科学院计算机网络信息中心 | Event extraction based sensitive information monitoring method |
| CN106557462A (en) * | 2016-11-02 | 2017-04-05 | 数库（上海）科技有限公司 | Name entity recognition method and system |
| CN109408806A (en) * | 2018-09-11 | 2019-03-01 | 中国电子科技集团公司第二十八研究所 | A kind of Event Distillation method based on English grammar rule |
| CN109582949A (en) * | 2018-09-14 | 2019-04-05 | 阿里巴巴集团控股有限公司 | Event element abstracting method, calculates equipment and storage medium at device |
| CN111723569A (en) * | 2020-05-21 | 2020-09-29 | 上海明略人工智能(集团)有限公司 | Event extraction method and device and computer readable storage medium |
-
2021
- 2021-06-02 CN CN202110612736.4A patent/CN113792083B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104572958A (en) * | 2014-12-29 | 2015-04-29 | 中国科学院计算机网络信息中心 | Event extraction based sensitive information monitoring method |
| CN106557462A (en) * | 2016-11-02 | 2017-04-05 | 数库（上海）科技有限公司 | Name entity recognition method and system |
| CN109408806A (en) * | 2018-09-11 | 2019-03-01 | 中国电子科技集团公司第二十八研究所 | A kind of Event Distillation method based on English grammar rule |
| CN109582949A (en) * | 2018-09-14 | 2019-04-05 | 阿里巴巴集团控股有限公司 | Event element abstracting method, calculates equipment and storage medium at device |
| CN111723569A (en) * | 2020-05-21 | 2020-09-29 | 上海明略人工智能(集团)有限公司 | Event extraction method and device and computer readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 韦向峰, 缪建明, 池哲洁, 袁毅: "基于概念层次网络的事件抽取研究", 网络新媒体技术, no. 04 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113792083B (en) | 2024-06-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110968684B (en) | Information processing method, device, equipment and storage medium | |
| JP7096919B2 (en) | Entity word recognition method and device | |
| CN110909165A (en) | Data processing method, device, medium and electronic equipment | |
| CN110020422A (en) | The determination method, apparatus and server of Feature Words | |
| JP7292657B2 (en) | DATA PROCESSING METHOD, DATA PROCESSING DEVICE, COMPUTER PROGRAM AND ELECTRONIC DEVICE | |
| CN107943792B (en) | Statement analysis method and device, terminal device and storage medium | |
| CN110866093A (en) | Machine question-answering method and device | |
| CN111309910A (en) | Text information mining method and device | |
| CN110929520B (en) | Unnamed entity object extraction method and device, electronic equipment and storage medium | |
| US20240086650A1 (en) | Relation extraction system and method adapted to financial entities and fused with prior knowledge | |
| CN107526721B (en) | Ambiguity elimination method and device for comment vocabularies of e-commerce products | |
| CN110781204A (en) | Identification information determination method, device, equipment and storage medium of target object | |
| CN113051914A (en) | Enterprise hidden label extraction method and device based on multi-feature dynamic portrait | |
| CN109947934A (en) | For the data digging method and system of short text | |
| CN113051362A (en) | Data query method and device and server | |
| CN113722438A (en) | Sentence vector generation method and device based on sentence vector model and computer equipment | |
| CN115994535A (en) | Text processing method and device | |
| CN111783427A (en) | Method, device, equipment and storage medium for training model and outputting information | |
| CN111354354B (en) | Training method, training device and terminal equipment based on semantic recognition | |
| CN113312924A (en) | Risk rule classification method and device based on NLP high-precision analysis label | |
| CN113590771A (en) | Data mining method, device, equipment and storage medium | |
| CN109543049B (en) | Method and system for automatically pushing materials according to writing characteristics | |
| CN116680401A (en) | Document processing method, document processing device, apparatus and storage medium | |
| CN113792083B (en) | Event extraction judging method and system | |
| CN113378826B (en) | Data processing method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |