Distantly Supervised Biomedical Named Entity Recognition with Dictionary Expansion Stateoftheart biomedical named entity recognition (BioNER) systems apply supervised machine learning models (i.e., relying on human effort for training data annotation) which are not easy to be generalized to new entity types and datasets. We propose a distantly supervised approach, AutoBioNER, that automatically recognizes biomedical entities from massive corpora with userinput dictionaries. AutoBioNER does not need any human annotated data. It relies on incomplete entity dictionaries to provide seeds for each entity type and performs a novel entity set expansion step for corpuslevel new entity recognition and dictionary completion. The expanded dictionaries are used as distant supervision to train a neural model for BioNER. Experimental results show that AutoBioNER achieves the best performance among the methods that only use dictionaries with no additional human effort on BioNER benchmark datasets. It is also demonstrated that the dictionary expansion step plays an important role in the great performances.

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%.