Named entity recognition for Chinese telecommunications field based on Char2Vec and Bi-LSTMs
Yu Wang, Bin Xia, Zheng Liu, Yun Li, Tao Li · 2017
Named Entity Recognition (NER) is a basic task in Natural Language Processing (NLP), which extracts the meaningful named entities from the text. Compared with the English NER, the Chinese NER is more challenge, since there is no tense in the Chinese language. Moreover, the omissions and the Internet catchwords in the Chinese corpus make the NER task more difficult. Traditional machine learning methods (e.g., CRFs) cannot address the Chinese NER effectively because they are hard to learn the complicated context in the Chinese language. To overcome the aforementioned problem, we propose a deep learning model Char2Vec+Bi-LSTMs for Chinese NER. We use the Chinese character instead of the Chinese word as the embedding unit, and the Bi-LSTMs is used to learn the complicated semantic dependency. To evaluate our proposed model, we construct the corpus from the China TELECOM FAQs. Experimental results show that our model achieves better performance than other baseline methods and the character embedding is more appropriate than the word embedding in the Chinese language.