关键词: 字词联合, 去噪机制, 长短期记忆网络, 中文命名实体识别

Abstract:

Chinese Named Entity Recognition（NER） is a basic task in the field of Chinese information processing, which can provide technical support for relation extraction, entity linking and knowledge graph. Compared with the traditional namedentity recognition methods, the model based on Bidirectional Long Short-Term Memory（BiLSTM） neural network has achieved good results in the task of Chinese NER. A Gated denoising mechanism is introduced to reduce the defect of BiLSTM-CRF model based on joint character-word learning, such as inaccurate feature extraction. The mechanism can fine tune the input character vector, automatically learn to filter or reduce the unimportant character information in the text, and retain more useful information for Chinese NER, so as to improve the recognition rate of the named entity. The test results on Resume and Weibo datasets show that this method effectively improves the results of Chinese NER.

Key words: joint character-word, denoising mechanism, Long Short-Term Memory（LSTM）, Chinese named entity recognition