关键词: 对比学习, 边界信息, 双编码器, 标签语义, 嵌套命名实体识别

Abstract: To address the following two major drawbacks of existing Contrastive Learning (CL) method in the nested Named Entity Recognition (NER) task: 1) Greedily enumerating candidate entities for CL lacked contextual semantics and boundary information. 2) It generated unnecessary noise and invalid information, increasing computational burden and weakening CL performance. A two-stage named entity recognition framework was proposed. In the first stage, candidate entity boundaries were generated by the boundary recognition model and integrated by the boundary integration module to minimize unnecessary negative candidates. Attention cues were inserted on both sides of the candidate entities to generate corresponding candidate entity text, allowing the model to perceive contextual semantics and boundary information. In the second stage, a bi-encoder framework mapped candidate entity texts and entity label annotations into the same vector representation space through CL, with the comparison object being sentences with attention cues rather than candidate entities. Compared with the Binder method, the proposed method improves the performance on three nested datasets, GENIA, ACE2005 and ACE2004, with F1 values of 1.22%, 3.42% and 2.31%, which verifies the effectiveness of the proposed method for task of nested named entity recognition

Key words: contrastive learning, boundary information, bi-encoder, label semantics, nested named entity