CWPC_BiAtt: Characterxe2x80x93Wordxe2x80x93Position Combined BiLSTMAttention for Chinese Named Entity Recognition Usually taken as linguistic features by PartOfSpeech (POS) tagging, Named Entity Recognition (NER) is a major task in Natural Language Processing (NLP). In this paper, we put forward a new comprehensiveembedding, considering three aspects, namely characterembedding, wordembedding, and posembedding stitched in the order we give, and thus get their dependencies, based on which we propose a new Characterxe2x80x93Wordxe2x80x93Position Combined BiLSTMAttention (CWPC_BiAtt) for the Chinese NER task. Comprehensiveembedding via the Bidirectional Llong ShortTerm Memory (BiLSTM) layer can get the connection between the historical and future information, and then employ the attention mechanism to capture the connection between the content of the sentence at the current position and that at any location. Finally, we utilize Conditional Random Field (CRF) to decode the entire tagging sequence. Experiments show that CWPC_BiAtt model we proposed is well qualified for the NER task on Microsoft Research Asia (MSRA) dataset and Weibo NER corpus. A high precision and recall were obtained, which verified the stability of the model. Positionembedding in comprehensiveembedding can compensate for attentionmechanism to provide position information for the disordered sequence, which shows that comprehensiveembedding has completeness. Looking at the entire model, our proposed CWPC_BiAtt has three distinct characteristics: completeness, simplicity, and stability. Our proposed CWPC_BiAtt model achieved the highest Fscore, achieving the stateoftheart performance in the MSRA dataset and Weibo NER corpus.

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%.