Named Entity Recognition with Gating Mechanism and Parallel BiLSTM
Yenan Yi, Yijie Bian · Journal of Web Engineering · 2021
In this paper, we propose a novel neural network for named entity recognition, which is improved in two aspects. On the one hand, our model uses a parallel BiLSTM structure to generate character-level word representations. By inputting character sequences of words into several independent and parallel BiLSTMs, we can obtain word representations from different representation subspaces, because the parameters of these BiLSTMs are randomly initialized. This method can enhance the expression abilities of character-level word representations. On the other hand, we use a two-layer BiLSTM with gating mechanism to model sentences. Since the features extracted by each layer in a multi-layer LSTM from texts contain different types of information, we use the gating mechanism to assign appropriate weights to the outputs of each layer, and take the weighted sum of these outputs as the final output for named entity recognition. Our model only changes the structure, does not need any feature engineering or external knowledge source, which is a complete end-to-end NER model. We used the CoNLL-2003 English and German datasets to evaluate our model and got better results compared with baseline models.