Towards Bootstrapping Biomedical Named Entity Recognition using Reinforcement Learning

Named entity recognition is one of the most fundamental problems in knowledge graph. In biomedical field, labeling high-quality biomedical entities requires plenty of linguistic knowledge due to abbreviation and specificity. Using dictionary is the simplest way for labeling, but it is difficult to obtain a versatile dictionary and usually a dictionary for one corpus is not suitable for another corpus due to bad transferability. Current mainstream recognition methods require lots of manpower, which is time-consuming and laborious. To handle this challenge, we present a novel approach to automatically recognize new biomedical entities. First, we use a small number of manually labeled biomedical entities as seeds to label some biomedical texts and learn their features autonomously. Then by using a tagger based on supervised learning and an instance selector based on reinforcement learning, we iteratively generate new biomedical entities. Experiment results demonstrate that our method can deal with biomedical named entity recognition and obtain significant performances in both English and Chinese biomedical datasets.