A Domain Knowledge-Enhanced LSTM-CRF Model for Disease Named Entity Recognition

Disease named entity recognition (NER) is a critical task for most biomedical natural language processing (NLP) applications. For example, extracting diseases from clinical trial text can be helpful for patient profiling and other downstream applications such as matching clinical trials to eligible patients. Similarly, disease annotation in biomedical articles can help information search engines to accurately index them such that clinicians can easily find relevant articles to enhance their knowledge. In this paper, we propose a domain knowledge-enhanced long short-term memory network-conditional random field (LSTM-CRF) model for disease named entity recognition, which also augments a character-level convolutional neural network (CNN) and a character-level LSTM network for input embedding. Experimental results on a scientific article dataset show the effectiveness of our proposed models compared to state-of-the-art methods in disease recognition.