Named Entity Recognition using Negative Sampling and Reinforcement Learning
Shi Peng, Yong Zhang, Zhengyun Wang, Dingkang Gao, Feng Xiong, Haoyang Zuo · 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2021
In this paper we propose a new named entity recognition (NER) model that combines negative sampling and reinforcement learning to reduce the impact of unlabeled entities and make full use of the labeled entities. Generally, there are a small number of correct entities in the training data which are not labeled inevitably. These unlabeled entities will greatly affect the performance of NER models. So in our model reinforcement learning is exploited to select the negative instances correctly and avoid those unlabeled entities for NER classifier because unlabeled entities are noisy data for negative instances. Then a NER classifier is trained on these negative instances and the manually labeled entities. Thus our model can be trained in the right direction under the guidance of the labeled entities and eliminate the influence of unlabeled entities. To evaluate our model, experiments are conducted on three public datasets, BioNLP11EPI, BioNLP13CG and BioNLP13GE, and with two kinds of embeddings, BERT and word2vec. Experiments show that our model can outperform the most of other models, and achieve comparable performance to the PFT model. Notably PFT need collect a large number of related sentences from unlabeled documents to pre-fine-tune BERT while our model need no additional corpus for training. Meanwhile when some entities are masked randomly, the performance of our model is significantly better than negative sampling model.