Using Deep Belief Nets for Chinese Named Entity Categorization
Yu Chen, You Ouyang, Wenjie Li, Dequan Zheng, Tiejun Zhao · 2010
Identifying named entities is essential in understanding plain texts. Moreover, the categories of the named entities are indicative of their roles in the texts. In this paper, we propose a novel approach, Deep Belief Nets (DBN), for the Chinese entity mention categorization problem. DBN has very strong representation power and it is able to elaborately self-train for discovering complicated feature combinations. The experiments conducted on the Automatic Context Extraction (ACE) 2004 data set demonstrate the effectiveness of DBN. It outperforms the state-of-the-art learning models such as SVM or BP neural network. 1