Named entity recognition model based on Multi‐BiLSTM and competition mechanism

Abstract

AbstractA named entity recognition (NER) model based on multiple bidirectional long short‐term memory networks (Multi‐BiLSTM) and competition mechanism (CM) is proposed. This model includes three parts: word vectorization module, learning module and application module. In the word vectorization module, the word vectors containing semantic features are generated based on BERT (Bidirectional Encoder Representations from Transformers). In the learning module, the multiple small‐scale BiLSTM are used to extract the contextual features of the word vectors synchronously. In the application module, the competition mechanism is adopted to select the relatively better models obtained in the learning module for recognizing entities. The results show the proposed model can reduce the parameters in the learning process, and the NER performance of the proposed model is better than that of the BERT‐BiLSTM‐CRF model. For example, the F1 can be increased by 1.02% and 0.39% for CoNLL2003 and Chinese resume datasets respectively.