A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition

TL;DR

This paper introduces a span-based joint model for recognizing both overlapped and discontinuous named entities, improving NER performance by combining span recognition with relation classification.

Contribution

A novel span-based approach that jointly recognizes overlapped and discontinuous entities and incorporates relation classification for improved NER accuracy.

Findings

Achieves high performance on benchmark datasets

Effectively recognizes both overlapped and discontinuous entities

Outperforms existing models in NER tasks

Abstract

Research on overlapped and discontinuous named entity recognition (NER) has received increasing attention. The majority of previous work focuses on either overlapped or discontinuous entities. In this paper, we propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly. The model includes two major steps. First, entity fragments are recognized by traversing over all possible text spans, thus, overlapped entities can be recognized. Second, we perform relation classification to judge whether a given pair of entity fragments to be overlapping or succession. In this way, we can recognize not only discontinuous entities, and meanwhile doubly check the overlapped entities. As a whole, our model can be regarded as a relation extraction paradigm essentially. Experimental results on multiple benchmark datasets (i.e., CLEF, GENIA and ACE05) show that our model is highly competitive for overlapped and discontinuous NER.