Improving Disease Named Entity Recognition for Clinical Trial Matching Disease named entity recognition (NER) is an important enabling technology to develop various downstream biomedical natural language processing applications. This is a challenging task, which requires addressing potential ambiguities due to variable contextual usage of the disease name mentions in clinical texts. In particular, clinical trial texts have unique complexities compared to patientfocused clinical reports or informationrich biomedical research articles, as they typically define drug testing eligibility requirements for patient cohorts via compound contextual and logical relationships. In this paper, we propose a novel disease NER model for clinical trial texts by using deep contextual embeddings with relevant domainspecific features, word embeddings, and character embeddings in a bidirectional long shortterm memory networkconditional random field (BiLSTMCRF) framework. Experiments and analyses on a clinical trial dataset and the benchmark NCBI scientific article dataset show the effectiveness of the proposed model.

Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition Abstract   With the rapid advancement of technology and the necessity of processing large amounts of data, biomedical Named Entity Recognition (NER) has become an essential technique for information extraction in the biomedical field. NER, which is a sequencelabeling task, has been performed using various traditional techniques including dictionary, rule, machine learning, and deep learningbased methods. However, as existing biomedical NER models are insufficient to handle new and unseen entity types from the growing biomedical data, the development of more effective and accurate biomedical NER models is being widely researched. Among biomedical NER models utilizing deep learning approaches, there have been only a few studies involving the design of highlevel features in the embedding layer. In this regard, herein, we propose a deep learning NER model that effectively represents biomedical word tokens through the design of a combinatorial feature embedding. The proposed model is based on Bidirectional Long ShortTerm Memory (biLSTM) with Conditional Random Field (CRF) and enhanced by integrating two different characterlevel representations extracted from a Convolutional Neural Network (CNN) and biLSTM. Additionally, an attention mechanism is applied to the model to focus on the relevant tokens in the sentence, which alleviates the longterm dependency problem of the LSTM model and allows effective recognition of entities. The proposed model was evaluated on two benchmark datasets, the JNLPBA and NCBIDisease, and a comparative analysis with the existing models is performed. The proposed model achieved a relatively higher performance with an F1score of 86.93% in case of NCBI Data, and a competitive performance for the JNLPBA with an F1score of 75.31%.