KCB-FLAT: Enhancing Chinese Named Entity Recognition with Syntactic Information and Boundary Smoothing Techniques

Zhenrong Deng, Zheng Huang, Shiwei Wei () and Jinglin Zhang
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Zhenrong Deng: Guangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China
Zheng Huang: Guangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China
Shiwei Wei: School of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin 541004, China
Jinglin Zhang: Guangxi Key Laboratory of Images and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China

Mathematics, 2024, vol. 12, issue 17, 1-19

Abstract: Named entity recognition (NER) is a fundamental task in Natural Language Processing (NLP). During the training process, NER models suffer from over-confidence, and especially for the Chinese NER task, it involves word segmentation and introduces erroneous entity boundary segmentation, exacerbating over-confidence and reducing the model’s overall performance. These issues limit further enhancement of NER models. To tackle these problems, we proposes a new model named KCB-FLAT, designed to enhance Chinese NER performance by integrating enriched semantic information with the word-Boundary Smoothing technique. Particularly, we first extract various types of syntactic data and utilize a network named Key-Value Memory Network, based on syntactic information to functionalize this, integrating it through an attention mechanism to generate syntactic feature embeddings for Chinese characters. Subsequently, we employed an encoder named Cross-Transformer to thoroughly combine syntactic and lexical information to address the entity boundary segmentation errors caused by lexical information. Finally, we introduce a Boundary Smoothing module, combined with a regularity-conscious function, to capture the internal regularity of per entity, reducing the model’s overconfidence in entity probabilities through smoothing. Experimental results demonstrate that the proposed model achieves exceptional performance on the MSRA, Resume, Weibo, and self-built ZJ datasets, as verified by the F1 score.

Keywords: named entity recognition; Chinese NER; syntactic information; word-boundary smoothing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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