Abstract
Named entity recognition (NER) is a typical sequential labeling problem that plays an important role in natural language processing (NLP) systems. In this paper, we discussed the details of applying a comprehensive model aggregating neural networks and conditional random field (CRF) on Chinese NER tasks, and how to discovery character level features when implement a NER system in word level. We compared the difference between Chinese and English when modeling the character embeddings. We developed a NER system based on our analysis, it works well on the ACE 2004 and SIGHAN bakeoff 2006 MSRA dataset, and doesn’t rely on any gazetteers or handcraft features. We obtained F1 score of 82.3% on MSRA 2006.
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