ToNER: Type-oriented Named Entity Recognition with Generative Language
  Model
Guochao Jiang Ziqin Luo Yuchen Shi Dixuan Wang Jiaqing Liang Deqing Yang
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.