ToNER: Type-oriented Named Entity Recognition with Generative Language Model

TL;DR

ToNER introduces a novel type-oriented generative framework for NER that leverages entity type prediction and auxiliary tasks to improve recognition accuracy, outperforming traditional tagging models.

Contribution

The paper proposes a new NER approach combining type matching, multi-task fine-tuning, and auxiliary tasks within a generative model to enhance entity recognition performance.

Findings

Effective in leveraging entity types for NER.

Outperforms traditional tagging-based models.

Validated on multiple NER benchmarks.

Abstract

In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task. It has also been found that the information related to entities, such as entity types, can prompt a model to achieve NER better. However, it is not easy to determine the entity types indeed existing in the given sentence in advance, and inputting too many potential entity types would distract the model inevitably. To exploit entity types' merit on promoting NER task, in this paper we propose a novel NER framework, namely ToNER based on a generative model. In ToNER, a type matching model is proposed at first to identify the entity types most likely to appear in the sentence. Then, we append a multiple binary classification task to fine-tune the generative model's encoder, so as to generate the refined representation of the input sentence. Moreover, we add an auxiliary task for the model to discover the entity types which further fine-tunes the model to output more accurate results. Our extensive experiments on some NER benchmarks verify the effectiveness of our proposed strategies in ToNER that are oriented towards entity types' exploitation.