A Residual BiLSTM Model for Named Entity Recognition
Gang Yang, Hongzhe Xu
Abstract
As one of the most powerful neural networks, Long Short-Term Memory (LSTM) is widely used in natural language processing (NLP) tasks. Meanwhile, the BiLSTM-CRF model is one of the most popular models for named entity recognition (NER), and many state-of-the-art models for NER are based on it. In this paper, we propose a new residual BiLSTM model and perform it with a conditional random field (CRF) layer together on NER tasks. Based on the most popular BiLSTM-CRF model, we replace the BiLSTM with our residual BiLSTM blocks to encode words or characters. We evaluate our model on Chinese and English datasets. We utilize both word2vec and BERT to generate word or character vectors. Furthermore, we conduct experiments to compare the performance of NER by using different structures of residual blocks. The experimental results show that our model can improve the performance of both Chinese and English NER effectively without introducing any external knowledge.