Entity relationship joint extraction method based on span and knowledge enhancement

Abstract

The invention discloses an entity relationship joint extraction method based on span and knowledge enhancement, and belongs to the technical field of information extraction and natural language processing. The method comprises the following steps: firstly, constructing a sample data set and labeling the data set; carrying out entity identification and relationship classification and specifically,for the labeled data, mapping words in a high-dimensional discrete space to a low-dimensional continuous space vector by a pre-training language model; carrying out span identification, filtering andrelationship classification by a span-based model; converting relationship classification into graph classification by utilizing a graph-based model, and introducing a syntactic dependency relationship so as to assist relationship judgment and classification; and performing joint training on an output result of the span-based model and an output result of the graph-based model, and identifying entities contained in the data and relationships among the entities. Syntactic information such as the dependency relationship is introduced into the end-to-end neural network model, so that the overlapping relationship is effectively identified, and the joint extraction accuracy of the entity relationship is improved.

Description

technical field [0001] The invention belongs to the technical fields of information extraction and natural language processing, and in particular relates to a method for joint extraction of entity relations based on span and knowledge enhancement. Background technique [0002] Extracting entities and their intrinsic relationships plays a crucial role in understanding text. Specifically, named entity recognition and relationship classification are particularly critical in judging the text structure for downstream tasks such as knowledge graph construction and knowledge-based question answering. Among them, named entity recognition refers to identifying entities with specific meaning in text and judging The type of the entity (person name, place name, institution name, proper noun, etc.), and the relationship classification is to judge the type of relationship between a given set of entity pairs. [0003] The traditional entity relationship extraction method is a pipeline pro...

AI Technical Summary

Problems solved by technology

[0004] Technical problem: In view of the poor effect of existing entity relationship extraction methods on entity relationship extraction, the present invention proposes a joint entity relationship extraction method based on span and knowledge enhancement, which can introduce syntax such as dependency relationships into the end-to-end neural network model Information, identify overlapping relationships, thereby improving the accuracy of entity relationship extraction

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