文档级实体关系抽取方法研究综述

doi:10.11896/jsjkx.211000057

Abstract

Abstract: As the core task of text mining and information extraction,entity relation extraction intends to identify and determine the specific relation between entity pairs from natural language texts,provides basic support for intelligent retrieval and semantic analysis,and helps to improve search efficiency.It is a research hotspot in the field of natural language processing.Compared with relation extraction from single sentence,documents contain richer entity relation semantics.Therefore,recently many new extraction methods have shifted their research focus from sentence-level to document-level,and achieved rich research results.This paper systematically summarizes the mainstream methods and research progress of document-level entity relation extraction in recent years.Firstly,the paper summarizes the problems and challenges of document-level relation extraction,and then introduces a variety of document-level relation extraction methods from three aspects:sequence based,graph based and pre-trained language model based.Finally,the data sets and experiments used by each method are compared and analyzed,and the possible research directions in the future are discussed and prospected.

Key words: Relation extraction, Document-level relation extraction, Deep learning, Graph neural network, Pre-trained language model

CLC Number:

TP391

FENG Jun, WEI Da-bao, SU Dong, HANG Ting-ting, LU Jia-min. Survey of Document-level Entity Relation Extraction Methods[J].Computer Science, 2022, 49(10): 224-242.

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