Recognizing irregular entities in biomedical text via deep neural networks

Named entity recognition (NER) is an important task for biomedical text mining. Most prior work focused on recognizing regular entities that consist of continuous word sequences and are not overlapped with each other. In this paper, we propose a neural network model called Bi-LSTM-CRF that consists of bidirectional (Bi) long short-term memories (LSTMs) and conditional random fields (CRFs) to identify regular entities and the components of irregular entities. Then the components are combined to build final irregular entities according to manually designed rules. Furthermore, we propose a novel model called NerOne that consists of the Bi-LSTM-CRF network and another Bi-LSTM network. The Bi-LSTM-CRF network performs the same task as the aforementioned model, and the Bi-LSTM network determines whether two components should be combined. Therefore, NerOne automatically combines the components instead of using manually designed rules. We evaluate our models on two datasets for recognizing regular and irregular biomedical entities. Experimental results show that, with less feature engineering, the performances of our models are comparable with those of state-of-the-art systems. We show that the method of automatically combining the components is as effective as the method of manually designing rules. Our work can facilitate the research on biomedical text mining.