所属单位：计算机科学与工程学院

发表刊物：PLOS ONE

项目来源：自选课题

关键字：Entity Relation Extraction, Span , Single-Stage

摘要：Extracting entities and relations from the unstructured text has attracted increasing attention in recent years. The existing work has achieved considerable results, yet it is difficult to solve entity overlap and exposure bias. To address cascading errors, exposure bias, and entity overlap in existing entity relation extraction approaches, we propose a joint entity relation extraction model (SMHS) based on a span-level multi-head selection mechanism,transforming entity relation extraction into a span-level multi-head selection problem. Our model uses span-tagger and span-embedding to construct span semantic vectors, utilizes LSTM and multi-head self-attention mechanism for span feature extraction, multi-head selection mechanism for span-level relation decoding, and introduces span classification task for multi-task learning to decode out the relation triad in a single-stage. Experiments on the classic English dataset NYT and the publicly available Chinese relationship extraction dataset DuIE 2.0 show that this method achieves better results than the baseline method,which verifies the effectiveness of this method. Source code and data are published here.

合写作者：韩东辰,郑肇谦,赵辉

第一作者：赵辉

论文类型：期刊论文

卷号：45

期号：4

页面范围：1

字数：1

ISSN号：1064-1246

是否译文：否

发表时间：2023-02-07