Dual-Enhancement Model of Entity Pronouns and Evidence Sentence for Document-Level Relation Extraction

Yurui Zhang, Boda Feng, Hui Gao, Peng Zhang, Wenmin Deng, Jing Zhang

Abstract: Document-level relation extraction (DocRE) aims to identify all relations between entities in different sentences within a document. Most works are committed to achieving more accurate relation prediction by optimizing model structure. However, the usage of entity pronoun information and extracting evidence sentences are limited by incomplete manual annotation data. In this paper, we propose a Dual-enhancement model of entity pronouns and EvideNce senTences (DeepENT), which efficiently leverages pronoun information and effectively extracts evidence sentences to improve DocRE. First, we design an Entity Pronouns Enhancement Module, which achieves co-reference resolution and automatic data fusion to enhance the completeness of entity information. Then, we define two types of evidence sentences and design heuristic rules to extract them, used in obtaining sentence-aware context embedding. In this way, we can logically utilize complete and accurate evidence sentence information. Experimental results reveal that our approach performs excellently on the Re-DocRED benchmark, especially in predicting inter-sentence expression relations.

External IDs:dblp:conf/iconip/ZhangFGZDZ23