A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
This addresses a specific challenge in natural language processing for tasks requiring complex entity recognition, though it is incremental by building on prior work focused on separate aspects.
The paper tackles the problem of recognizing both overlapped and discontinuous named entities in text, proposing a span-based model that achieves competitive performance on benchmark datasets like CLEF, GENIA, and ACE05.
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.