Local Feature Enhancement for Nested Entity Recognition Using a Convolutional Block Attention Module
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Published:2023-08-13
Issue:16
Volume:13
Page:9200
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Deng Jinxin1, Liu Junbao1, Ma Xiaoqin12, Qin Xizhong12, Jia Zhenhong12
Affiliation:
1. College of Information Science and Engineering, Xinjiang University, Urumqi 830049, China 2. Xinjiang Signal Detection and Processing Key Laboratory, Urumqi 830049, China
Abstract
Named entity recognition involves two main types: nested named entity recognition and flat named entity recognition. The span-based approach treats nested entities and flat entities uniformly by classifying entities on a span representation. However, the span-based approach ignores the local features within the entities and the relative position features between the head and tail tokens, which affects the performance of entity recognition. To address these issues, we propose a nested entity recognition model using a convolutional block attention module and rotary position embedding for local features and relative position features enhancement. Specifically, we apply rotary position embedding to the sentence representation and capture the semantic information between the head and tail tokens using a biaffine attention mechanism. Meanwhile, the convolution module captures the local features within the entity to generate the span representation. Finally, the two parts of the representation are fused for entity classification. Extensive experiments were conducted on five widely used benchmark datasets to demonstrate the effectiveness of our proposed model.
Funder
Xinjiang Uygur Autonomous Region
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference49 articles.
1. Research on named entity recognition;Zhang;Comput. Sci.,2005 2. Electrophysiological evidence for two steps in syntactic analysis: Early automatic and late controlled processes;Hahne;J. Cogn. Neurosci.,1999 3. Babych, B., and Anthony, H. (2003, January 13). Improving machine translation quality with automatic named entity recognition. Proceedings of the 7th International EAMT Workshop on MT and Other Language Technology Tools, Improving MT through Other Language Technology Tools, Resource and Tools for Building MT at EACL 2003, Budapest, Hungary. 4. Soricut, R., and Eric, B. (2004, January 2–7). Automatic question answering: Beyond the factoid. Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004, Boston, MA, USA. 5. Zhang, J., Xie, J., Hou, W., Tu, X., Xu, J., Song, F., and Lu, Z. (2012). Mapping the knowledge structure of research on patient adherence: Knowledge domain visualization based co-word analysis and social network analysis. PLoS ONE, 7.
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