@inproceedings{c2301ddf214e4c728a5320979c1a2ca2,
title = "Using deep belief nets for Chinese named entity categorization",
abstract = "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.",
author = "Yu Chen and You Ouyang and Wenjie Li and Dequan Zheng and Tiejun Zhao",
note = "Publisher Copyright: {\textcopyright} 2010 Association for Computational Linguistics; 2010 Named Entities Workshop, NEWS 2010 at the 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 - Proceedings of the Workshop ; Conference date: 16-07-2010",
year = "2010",
language = "English",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "102--109",
editor = "A Kumaran and Haizhou Li",
booktitle = "NEWS 2010 - 2010 Named Entities Workshop at the 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 - Proceedings of the Workshop",
address = "United States",
}