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

• Published in: Mathematics (Basel) Vol. 12; no. 17; p. 2714
• Main Authors: Deng, Zhenrong, Huang, Zheng, Wei, Shiwei, Zhang, Jinglin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2024
• Subjects: Chinese NER; Data smoothing; Datasets; Internet; named entity recognition; Natural language processing; Probability; Recognition; Regularity; Segmentation; Semantics; Smoothing; syntactic information; word-boundary smoothing; Words (language); China
• DOI: 10.3390/math12172714
• EISSN: 2227-7390

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.