Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Document-self expansion for text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Liveclassifier: creating hierarchical text classifiers through web corpora
Proceedings of the 13th international conference on World Wide Web
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
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Automatically classifying the Web directories is an effective way to manage Web information. However, our experiments showed that the state-of-the-art text classification technologies could not lead to acceptable performance in this task. Due to our analysis, the main problem is the lack of effective training data in rare categories of Web directories. To tackle this problem, we proposed a novel technology named Site Abstraction to synthesize new training examples from the website of the existing training document. The main idea is to propagate features through parent-child relationship in the sitemap tree. Experiments showed that our method significantly improved the classification performance.