Research SharingOriented Functional Neuroimaging Named Entity Recognition Named entity recognition is a basic and core task of information extraction on functional neuroimaging literatures. However, existing researches only focus on cognitive states and brain regions, and are far from effective research sharing. This paper proposes a research sharingoriented approach for functional neuroimaging named entity recognition. The nine most representative entity categories were defined by analyzing the characteristics of taskbased functional neuroimaging researches, and a multicategory named entity recognition method was designed based on BiLSTMCNN. An experiment was performed on literatures obtained from the journal PLoS One. The experimental results show that the precision and recall rates of the proposed method can reach 94.50% and 95.56%, and are obviously superior to existing methods of functional neuroimaing named entity recognition.

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%.