Type-supervised sequence labeling based on the heterogeneous star graph for named entity recognition

TL;DR

This paper introduces a novel type-supervised sequence labeling model based on a heterogeneous star graph that effectively captures nested entities in named entity recognition, achieving state-of-the-art results.

Contribution

It proposes a new graph-based approach with a hybrid attention mechanism and an extended annotation scheme for improved nested entity recognition.

Findings

Achieved state-of-the-art performance on flat and nested NER datasets.

Effectively captures nested entities with a multi-layer labeling strategy.

Demonstrated the effectiveness of the heterogeneous star graph model.

Abstract

Named entity recognition is a fundamental task in natural language processing, identifying the span and category of entities in unstructured texts. The traditional sequence labeling methodology ignores the nested entities, i.e. entities included in other entity mentions. Many approaches attempt to address this scenario, most of which rely on complex structures or have high computation complexity. The representation learning of the heterogeneous star graph containing text nodes and type nodes is investigated in this paper. In addition, we revise the graph attention mechanism into a hybrid form to address its unreasonableness in specific topologies. The model performs the type-supervised sequence labeling after updating nodes in the graph. The annotation scheme is an extension of the single-layer sequence labeling and is able to cope with the vast majority of nested entities. Extensive experiments on public NER datasets reveal the effectiveness of our model in extracting both flat and nested entities. The method achieved state-of-the-art performance on both flat and nested datasets. The significant improvement in accuracy reflects the superiority of the multi-layer labeling strategy.