Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings
Nanyun Peng, Mark H. Dredze · 2015
We consider the task of named entity recognition for Chinese social media. The long line of work in Chinese NER has fo-cused on formal domains, and NER for social media has been largely restricted to English. We present a new corpus of Weibo messages annotated for both name and nominal mentions. Additionally, we evaluate three types of neural embeddings for representing Chinese text. Finally, we propose a joint training objective for the embeddings that makes use of both (NER) labeled and unlabeled raw text. Our meth-ods yield a 9 % improvement over a state-of-the-art baseline. 1