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Improving automatic Chinese text categorization by error correction by Jhy-Jong Tsay; Jing-Doo Wang is a scholarly article available to read on EtoBox.

What is Improving automatic Chinese text categorization by error correction about?

In this paper we use the miss-classified news in training data as a feedback to improve the classification accuracy. We isolate the miss-classified news from the news of original classes to form new subclasses, and modify Rocchio linear classifier by using the subclasses to form new prototype vectors such that there are more than one centroid to represent each class. We have two methods, error threshold and entropy threshold, to evaluate whether the isolation of miss-classified news is worthy or not. Experimental result shows that our approaches improves Rocchio's micro-level accuracy and achieves similar performance as kNN, but with less classification time. On the other hand, with the entropy of missclassified news, we can figure out the ambiguity between classes and sketch the diagram of the relationship between classes as a suggestion to reorganize the structure of classes for news in the future.

Author

Jhy-Jong Tsay; Jing-Doo Wang

Publisher

ACM

Published

2000

Language

EN