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Text entity relationship extraction method and model training method

An entity relationship and entity technology, applied in unstructured text data retrieval, special data processing applications, instruments, etc., can solve problems such as poor performance, loss of related information, weakened semantic extraction ability, etc., to improve accuracy and strengthen expression , Entity relationship extraction accurate effect

Active Publication Date: 2020-06-26
TENCENT TECH (SHENZHEN) CO LTD
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AI Technical Summary

Problems solved by technology

However, the increase in the length of the text and the increase in the number of entities in the text will weaken the semantic extraction ability of these pre-trained models for the input text, causing the features extracted by the model to lose the correlation information between words, resulting in the existing technology in long sentences or spanning Poor performance in entity relationship extraction of sentences

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  • Text entity relationship extraction method and model training method
  • Text entity relationship extraction method and model training method
  • Text entity relationship extraction method and model training method

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Embodiment Construction

[0063] The present application will be further described below in conjunction with the accompanying drawings and specific embodiments. The described embodiments should not be regarded as limiting the present application, and all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present application.

[0064] In the following description, references to "some embodiments" describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or a different subset of all possible embodiments, and Can be combined with each other without conflict.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application, a...

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Abstract

The invention discloses a text entity relationship extraction method and a model training method, and can be applied to a natural language processing technology in the field of artificial intelligence; according to the invention, the graph state recurrent neural network and the BERT model are combined, a first vector used for representing semantic features of the text and a second vector used forrepresenting dependency relationship features of the text are extracted from the text; the first vector and the second vector are spliced and then classified; the relationship extraction of the entitypairs is enabled to obtain relatively high accuracy in long sentence and cross-sentence application scenes; the problem of insufficient accuracy in application scenes such as long sentences and crosssentences in the prior art is solved, in addition, in the model training stage, based on the preset rules and the pre-training model, a large amount of annotation data is produced in a remote supervision mode, and a large amount of accurate training data can be obtained at a low cost. Therefore, the method can be widely applied to the natural language processing technology.

Description

technical field [0001] The present application relates to natural language processing technology, especially a text entity relationship extraction, model training method, device and storage medium. Background technique [0002] With the development of artificial intelligence (AI) technology and the continuous growth of application requirements in specific fields, the research on applying artificial intelligence technology to specific fields such as the medical field has been developed. Among them, natural language processing (Natural Language Processing, NLP) technology is an important branch of artificial intelligence technology. Among them, in natural language processing technology, the construction of Knowledge Graph plays an important role in the application of artificial intelligence. For example, artificial intelligence can use knowledge graphs to complete tasks such as retrieval and question answering. [0003] The knowledge graph is composed of the relationship bet...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/295G06F40/30G06F16/36
CPCG06F16/367
Inventor 陈曦卢睿轩文瑞孙继超刘羽
Owner TENCENT TECH (SHENZHEN) CO LTD
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