文献类型：期刊文献

英文题名：HTMapper: Bidirectional Head-Tail Mapping for Nested Named Entity Recognition

作者：Zhao, Jin[1]; Li, Zhixu[1]; Xiao, Yanghua[2]; Liang, Jiaqing[3]; Liu, Jingping[4]

机构：[1] Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China; [2] Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Fudan-Aishu Cognitive Intelligence Joint Research Center, Shanghai, China; [3] School of Data Science, Fudan University, Shanghai, China; [4] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2023

起止页码：3433

外文期刊名：International Conference on Information and Knowledge Management, Proceedings

收录：EI(收录号：20234815122680)

语种：英文

外文关键词：Character recognition - Natural language processing systems

摘要：Nested named entity recognition (Nested NER) aims to identify entities with nested structures from the given text, which is a fundamental task in Natural Language Processing. The region-based approach is the current mainstream approach, which first generates candidate spans and then classifies them into predefined categories. However, this method suffers from several drawbacks, including over-reliance on span representation, vulnerability to unbalanced category distribution, and inaccurate span boundary detection. To address these problems, we propose to model the nested NER problem into a head-tail mapping problem, namely, HTMapper, which detects head boundaries first and then models a conditional mapping from head to tail under a given category. Based on this mapping, we can find corresponding tails under different categories for each detected head by enumerating all entity categories. Our approach directly models the head boundary and tail boundary of entities, avoiding over-reliance on the span representation. Additionally, Our approach utilizes category information as an indicator signal to address the imbalance of category distribution during category prediction. Furthermore, our approach enhances the detection of span boundaries by capturing the correlation between head and tail boundaries. Extensive experiments on three nested NER datasets and two flat NER datasets demonstrate that our HTMapper achieves excellent performance with F1 scores of 89.09%, 88.30%, 81.57% on ACE2004, ACE2005, GENIA, and 94.26%, 91.40% on CoNLL03, OntoNotes, respectively. ? 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.