Augmented Disentanglement and Aggregation for Nested Named Entity Recognition

Nested named entity recognition (Nested NER) aims to identify and classify all possible span entities within a text. Existing approaches primarily rely on enumeration techniques and span-based methods to address the challenge of overlapping entities. However, these methods often overlook the structural distribution and inherent semantics of entities, making them susceptible to issues such as ambiguous start-end tokens, blurred entity boundaries, and a high degree of token overlap. In this paper, we propose a novel strategy we name Augmented Disentanglement and Aggregation for Nested Named Entity Recognition (ada-NER), which employs a series of augmentation strategies to extract nested entities from text. Specifically, we first reformulate the nested NER task as a problem of disentangling and aggregating the relationships between span recognition and type classification. This formulation enables the model to capture fine-grained and comprehensive contextual interactions within sentences. Furthermore, span entities are recognized through the joint modeling of hard boundary encoding and soft edge encoding, while type classification is enhanced by incorporating both intra and inter distribution relationships as well as dependency information. Finally, we introduce a well-designed fusion mechanism to obtain entity representation within a shared space. Extensive experiments on two public datasets demonstrate the effectiveness of our proposed model, which consistently outperforms competitive baseline methods.