ABSTRACT Biomedical named entity recognition (BNER) is the basis of biomedical text mining and one of the core sub-tasks of information extraction. Previous BNER models based on conventional machine learning rely on time-consuming feature engineering. Though most neural network methods improve the problems with automatic learning, they cannot pay attention to the significant areas when capturing features. In this paper, we propose an attention-based BiLSTM-CRF model. First, this model adopts a bidirectional long short-term memory network (BiLSTM) to obtain more complete context information. At the same time, the attention mechanism is proposed to improve the vector representations in BiLSTM. We design different attention weight redistribution methods and fuse them. It effectively prevents the significant information loss when extracting features. Finally, combining BiLSTM with conditional random field (CRF) layer effectively solves the problems of the inability to handle the strong dependence of tags in the sequence. With the simple architecture, our model achieves a reasonable performance on the JNLPBA corpus. It obtains an F1-score of 73.50. Our model can enhance the ability of the neural network to extract significant information and does not rely on any feature engineering, with only general pre-training word vectors. It makes our model have high portability and extendibility. INDEX TERMS Biomedical text, named entity recognition, attention mechanism, long short-term memory, conditional random field.
To pay attention to the significant areas when capturing features, Wei et al. REF proposed an attention-based BILSTM-CRF model, and their model obtained an F1-score of 73.50% on JNLPBA corpus.
Named Entity Recognition From Biomedical Texts Using a Fusion Attention-Based BiLSTM-CRF
IEEE Access
IEEE Access
Computer Science