Improving automatic Chinese text categorization by error correction

2000 ◽  
Author(s):  
Jhy-Jong Tsay ◽  
Jing-Doo Wang
Keyword(s):  
Error Correction ◽  
Chinese Text ◽  
Text Categorization

Chinese Text Categorization via Bottom-Up Weighted Word Clustering

2015 ◽  
Vol 11 (1) ◽  
pp. 50-61
Author(s):  
Yu-Chieh Wu
Keyword(s):  
Chinese Text ◽  
Conceptual Knowledge ◽  
Text Categorization ◽  
Good Method ◽  
Latent Semantic Indexing ◽  
Test Collection ◽  
Bag Of Words ◽  
Efficiency And Effectiveness ◽  
Word Clustering ◽  
Term Clustering

Most of the researches on text categorization are focus on using bag of words. Some researches provided other methods for classification such as term phrase, Latent Semantic Indexing, and term clustering. Term clustering is an effective way for classification, and had been proved as a good method for decreasing the dimensions in term vectors. The authors used hierarchical term clustering and aggregating similar terms. In order to enhance the performance, they present a modify indexing with terms in cluster. Their test collection extracted from Chinese NETNEWS, and used the Centroid-Based classifier to deal with the problems of categorization. The results had shown that term clustering is not only reducing the dimensions but also outperform than bag of words. Thus, term clustering can be applied to text classification by using any large corpus, its objective is to save times and increase the efficiency and effectiveness. In addition to performance, these clusters can be considered as conceptual knowledge base, and kept related terms of real world.

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