Local Feature Enhancement for Nested Entity Recognition Using a Convolutional Block Attention Module - Jinxin Deng; Junbao Liu; Xiaoqin Ma; Xizhong Qin; Zhenhong Jia (Applied Sciences, 2023)
Named entity recognition involves two main types: nested named entity recognition and flat named entity recognition. The span-based approach treats nested entities and flat entities uniformly by classifying entities on a span representation. However, the span-based approach ignores the local features within the entities and the relative position features between the head and tail tokens, which affects the performance of entity recognition. To address these issues, we propose a nested entity recognition model using a convolutional block attention module and rotary position embedding for local features and relative position features enhancement. Specifically, we apply rotary position embedding to the sentence representation and capture the semantic information between the head and tail tokens using a biaffine attention mechanism. Meanwhile, the convolution module captures the local features within the entity to generate the span representation. Finally, the two parts of the representation are fused for entity classification. Extensive experiments were conducted on five widely used benchmark datasets to demonstrate the effectiveness of our proposed model.