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.
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science