Entity relationship joint extraction method

An entity relationship and relationship collection technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems of unable to learn sentence context information, uncorrected extraction results, unable to identify overlapping relationships, etc.

Pending Publication Date: 2020-02-11
HOHAI UNIV
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Problems solved by technology

[0007] In view of the problems existing in the existing joint learning method that the overlapping relationship cannot be identified, the richer context information in the sentence cannot be learned, and the extraction results have not been corrected, the purpose of the present invention is to propose a multi-label labeling and compound attention mechanism. The joint entity-relationship extraction method can realize direct modeling of triples and avoid the error accumulation problem caused by extracting entities and relationships between entities separately. It is an effective tool for information extraction and natural language processing

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[0070] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0071] Such as figure 1 As shown, the entity-relationship joint extraction method of the present invention includes the following steps: collect researched corpus data, and then remove the sentence whose relation label is "None", for example, {"E1":"Minnesota","E2" exists in the sentence: "TimPawlenty", "label": "None"} such a triplet, where Minnesota is a place name, Tim Pawlenty is a person's name, label (label) indicates that the relationship between these two entities is "None", and None indicates that the relationship between the two entities is "None". There is no relationship between entities. Perform multi-label labeling on the remaining sentences to form a training set; input the multi-label-labeled sentences into the joint extraction m...

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Abstract

The invention discloses an entity relationship joint extraction method based on multi-label labeling and a composite attention mechanism, which comprises the following steps: collecting corpus data for research, then removing sentences of which the relationship labels are 'None', and performing multi-label labeling on the rest sentences to form a training set; inputting the sentences subjected tomulti-label labeling into a joint extraction model, identifying entities contained in the sentences and relationships among the entities through the joint extraction model, and constructing a triple;and correcting the extracted triples by utilizing a relationship alignment model so as to adapt to multi-label labeling of (head entity E1 and tail entity E2) entity pairs. The method has the advantages that the accuracy of triple extraction can be effectively improved, and the method is an effective tool for information extraction of unstructured data.

Description

technical field [0001] The invention relates to the technical fields of information extraction and natural language processing, in particular to a method for joint extraction of entity-relationships. Background technique [0002] With the rapid development of Internet technology, the amount of data that people need to process has increased dramatically. How to quickly and efficiently extract entities and the relationship information between entities from these open-field texts has become an important problem that needs to be solved urgently. Entity relationship extraction is a core task of information extraction for unstructured data. Its main goal is to simultaneously detect entities from text and identify the semantic relationship between entity pairs. It is widely used in knowledge graph construction, information retrieval, and dialogue generation. and question answering systems. At present, entity relationship extraction generally adopts two frameworks: pipeline method ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/295G06F40/211G06N3/04G06N3/08
CPCG06N3/0463G06N3/08G06N3/045
Inventor 冯钧杭婷婷李晓东陆佳民严乐朱跃龙
Owner HOHAI UNIV
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