关键词: 命名实体识别, 跨度分类, 多目标学习, 边界生成, 神经网络

Abstract: Named entity recognition aims to identify predefined entity types from unstructured text. Span-based named entity recognition methods recognize entities from among all enumerated spans. However, due to adjacent spans in the text share contextual semantics, this leads to semantic ambiguity at span boundaries and makes it difficult for models to capture semantic dependencies between spans. To address the issue of ambiguous boundary semantics between spans, a multi-objective learning named entity recognition model with boundary generation was proposed. The model was trained through a multi-objective learning approach that jointly combined named entity recognition task with boundary generation task. The boundary generation task served as an auxiliary task. It helped the model network focus on the boundary information, thus enhancing the semantic clarity of boundaries and improving the performance of named entity recognition. To validate the effectiveness of the model, tests were conducted on the ACE04, ACE05, and Genia datasets and achieved F1 scores of 87.83%, 86.90%, and 81.65%, respectively. The experimental results demonstrate that the proposed method improves the accuracy of named entity recognition.

Key words: named entity recognition, span classification, multi-objective learning, boundary generation, neural networks