Entity relationship joint extraction method

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 ...

AI Technical Summary

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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