融合边界交互信息的命名实体识别方法

doi:10.16088/j.issn.1001-6600.2024092703

Abstract

Abstract: As a basic task in natural language processing, named entity recognition (NER) can effectively identify and classify named entities in text. Some progress has been made in entity recognition with span-based methods, but the quality differences between candidate spans are often overlooked. To tackle the problem, a named entity recognition method that fuses boundary interaction information is proposed. A boundary interaction module is used to evaluate the semantic associations and interaction strengths between boundaries, and a boundary interaction information matrix is generated. This matrix is used to identify potential semantic connections between boundaries, guiding the model to recognize and mark high-quality candidate spans. Additionally, a multi-scale dilated convolution module is integrated to reduce the impact of non-entity noise by utilizing the semantic relationships between spans. It is demonstrated through experiments that the method achieves F₁ scores of 89.78%, 87.37%, and 72.10% on the ACE2005 Chinese dataset, ACE2005 English dataset, and Weibo dataset, respectively. These results represent improvements of 0.67, 0.95, and 0.69 percentage points over baseline models, validating the effectiveness of the proposed method for named entity recognition.

Key words: natural language processing, named entity recognition, information extraction, boundary interaction

CLC Number: TP391.1

HE Ankang, CHEN Yanping, HU Ying, HUANG Ruizhang, QIN Yongbin. Fusing Boundary Interaction Information for Named Entity Recognition[J].Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(3): 1-11.

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Fusing Boundary Interaction Information for Named Entity Recognition

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