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Method of entity relation extraction based on neural network

An entity relationship, neural network technology, applied in the field of algorithm application, can solve the problem that it is difficult for users to quickly obtain information and knowledge resources

Active Publication Date: 2018-12-21
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

[0002] With the vigorous development of the Internet, the number of users increases rapidly, and the information generated by users online continues to increase. The traditional information retrieval method of returning to the retrieval page has been difficult to meet the needs of users to obtain information and knowledge resources comprehensively and quickly.

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  • Method of entity relation extraction based on neural network

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Embodiment Construction

[0033] The present invention will be further described below in conjunction with the accompanying drawings and embodiments, but the present invention is not limited thereto.

[0034] Such as figure 1 As shown, a neural network-based entity relationship extraction method extracts entities by constructing a neural network model, and then uses a classification algorithm to classify the entities extracted by the model to complete the extraction of entity relationships, which specifically includes the following steps:

[0035] 1) Preprocessing the training set:

[0036] 1-1) Segment the text and text of the training set to achieve the effect of separating words; for example, split "German Chancellor Gauck's visit to China" into "Germany B-ORG Country I-ORG General Manager O Gao B-PER gram I-PER visit O to ask the training text labeled "B-ORG country I-ORG" in P, such as figure 2 shown.

[0037] 1-2) Convert the separated words into a dictionary, each word has a corresponding nu...

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Abstract

The invention discloses an entity relation extraction method based on a neural network, using the algorithm of machine learning and neural network model, Input a Chinese sentence into the program model, the model will give a special label to the entity words or statements, that is, the entity in the text can be extracted, and then through a classification algorithm for the extracted entity to do relationship classification, entity relationship classification is completed. Specifically, assign an ID to each word that appears in the Chinese text, Then the IDs corresponding to these sentences aretransformed into input vectors of the neural network model, and the results obtained through bilstm and CRF layer are mapped to corresponding entity tags to complete entity extraction. Finally, the entities in the text are classified by machine learning classification algorithm, and finally such a triple form of an entity of the entities-relationsientities is obtained. . This method only needs training text and input statements to complete the extraction of relational entities, which is a flexible and convenient method.

Description

technical field [0001] The invention relates to the application of algorithms in machine learning and deep learning, as well as related Chinese language processing methods, in particular to a neural network-based entity relationship extraction method. Background technique [0002] With the vigorous development of the Internet, the number of users increases rapidly, and the information generated by users online continues to increase. The traditional information retrieval method of returning to the retrieval page has been difficult to meet the needs of users for comprehensive and rapid access to information and knowledge resources. As an important part of information extraction, entity relationship extraction automatically extracts the structured information of entity relationship tuples from natural language, thereby providing users with a more intelligent information retrieval method, which can help users quickly understand and master the growing information in the Internet. ...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 钟艳如赵蕾先姜超豪谢庆博罗笑南
Owner GUILIN UNIV OF ELECTRONIC TECH
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