Title :
A multi-label Chinese text categorization system based on boosting algorithm
Author :
Chen, Junli ; Zhou, Xuezhong ; Wu, Zhaohui
Author_Institution :
Coll. of Compute Sci., Zhejiang Univ., China
Abstract :
This paper presents a multi-label Chinese text categorization system based on Chinese character features and boosting algorithm. This system has been successfully evaluated on the TCM-MED dataset provided by China Academy of traditional Chinese medicine (TCM) and the Reuters-21578 benchmark. We suggest that the TCM-MED dataset can be used as a standard corpus for the Chinese text categorization tasks. We have also carried out experiments to compare the performance of the boosting algorithm with two other traditional algorithms on the same datasets. The results indicate that for the design of a multi-label Chinese text categorization system, the boosting algorithm has a high performance and outperforms the other two algorithms.
Keywords :
classification; natural languages; text analysis; China Academy; Chinese character features; Reuters-21578 benchmark; TCM-MED dataset; boosting algorithm; multilabel Chinese text categorization system; traditional Chinese medicine; Algorithm design and analysis; Boosting; Dispatching; Document handling; Educational institutions; Information processing; Machine learning; Machine learning algorithms; Nearest neighbor searches; Text categorization;
Conference_Titel :
Computer and Information Technology, 2004. CIT '04. The Fourth International Conference on
Print_ISBN :
0-7695-2216-5
DOI :
10.1109/CIT.2004.1357350