Exploring Recurrent Neural Networks to Detect Named Entities from Biomedical Text

Abstract

Biomedical named entity recognition (bio-NER) is a crucial and basic step in many biomedical information extraction tasks. However, traditional NER systems are mainly based on complex hand-designed features which are derived from various linguistic analyses and maybe only adapted to specified area. In this paper, we construct Recurrent Neural Network to identify entity names with word embeddings input rather than hand-designed features. Our contributions mainly include three aspects: (1) we adapt a deep learning architecture Recurrent Neural Network (RNN) to entity names recognition; (2) based on the original RNNs such as Elman-type and Jordan-type model, an improved RNN model is proposed; (3) considering that both past and future dependencies are important information, we combine bidirectional recurrent neural networks based on information entropy at the top layer. The experiments conducted on the BioCreative II GM data set demonstrate RNN models outperform CRF and deep neural networks (DNN), furthermore, the improved RNN model performs better than two original RNN models and the combined method is effective.