A two-layer BiLSTM model with linear gating for Chinese named entity recognition

Chinese named entity recognition (CNER) is one of the most fundamental tasks in natural language processing (NLP), and is key to extracting information from unstructured texts. In recent years, advances in neural network models and pretrained word-level information embedding techniques have played a driving role in the development of NLP. In this context, how to make full use of word vectors to extract information has become one of the research emphases. The diversity of Chinese expressions and the irregular expressions of texts lead to poor recognition results. This paper proposes a two-layer BiLSTM network model with linear gating logic to enhance the model's learning effect of word vectors within sentences and word memory. The aim is to solve the problem of gradient disappearance and improve the model's generalization ability and entity recognition. Through experiments, our model proved effective on three Chinese benchmark datasets: MSRA, the People's Daily Corpus (PRF), and Boson. The precision of NER performs best among similar models. In addition, using the lab-constructed medical dataset of Chinese Drugs for the Heart for testing, our model outperforms the existing BiLSTM model. Finally, statistical analysis of the changes in F1 during training demonstrated faster convergence of our model.