Span-Prototype Graph Based on Graph Attention Network for Nested Named Entity Recognition

Abstract

Named entity recognition, a fundamental task in natural language processing, faces challenges related to the sequence labeling framework widely used when dealing with nested entities. The span-based method transforms nested named entity recognition into span classification tasks, which makes it an efficient way to deal with overlapping entities. However, too much overlap among spans may confuse the model, leading to inaccurate classification performance. Moreover, the entity mentioned in the training dataset contains rich information about entities, which are not fully utilized. So, in this paper, a span-prototype graph is constructed to improve span representation and increase its distinction. In detail, we utilize the entity mentions in the training dataset to create a prototype for each entity category and add prototype loss to adapt the span to its similar prototype. Then, we feed prototypes and span into a graph attention network (GAT), enabling span to automatically learn from different prototypes, which integrate the information about entities into the span representation. Experiments on three common nested named entity recognition datasets, including ACE2004, ACE2005, and GENIA, show that the proposed method achieves 87.28%, 85.97%, and 79.74% F1 scores on ACE2004, ACE2005, and GENIA, respectively, performing better than baselines.