An InterFormer-BERT Chinese named entity recognition model with enhanced boundary
Authors: 
Yue Li, Tao ZuoAuthors Info & Claims
Yue Li, Tao ZuoAuthors Info & ClaimsPages 388 - 394
Abstract
Chinese Named Entity Recognition (NER) is more challenging compared to English, mainly because Chinese lacks clear word boundaries and natural separators. Certainly! In recent times, there has been a trend in utilizing pre-trained models augmented with adapter modules to create novel models for diverse NLP tasks, showcasing improved effectiveness. In this paper, we propose a boundary enhancement approach to improve Chinese NER. on the one hand, the representation of the internal dependencies of phrases is enhanced by an additional InterFormer (Inter Attention Network), which is then integrated as an adapter directly between the Transformer layers of BERT, which helps to enable a closer integration of word boundaries and semantic information, and on the other hand, to adding special tokens at the head and tail of entities (i.e., boundaries) as a supplementary task, this paper presents a novel framework for learning boundary information and performing entity recognition jointly. Experimental results on the Weibo, Resume, OntoNotes, and MRSA corpora validate the effectiveness of the proposed approach.
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Index Terms
- An InterFormer-BERT Chinese named entity recognition model with enhanced boundary
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Published In
March 2024
676 pages
ISBN:9798400718212
DOI:10.1145/3672919
Copyright © 2024 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 July 2024
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CSAIDE 2024
CSAIDE 2024: 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy
March 1 - 3, 2024
Nanjing, China
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