期刊名称：Journal of Information and Organizational Sciences
印刷版ISSN：1846-3312
电子版ISSN：1846-9418
出版年度：1999
卷号：23
期号：2
页码：123-131
出版社：Faculty of Organization and Informatics University of Zagreb
摘要：This paper describes the usage of machine learning techniques to assign keywords to documents. The large hierarchy of documents available on the Web, the Yahoo hierarchy, is used here as a real-world problem domain. Machine learning techniques developed for learning on text data are used here in the hierarchical classification structure. The high number of features is reduced by taking into account the hierarchical structure and using a feature subset selection based on the method used in information retrieval. Documents are represented as word-vectors that include word sequences (n-grams) instead of just single words. The hierarchical structure of the examples and class values is taken into account when defining the subproblems and forming training examples for them. Additionally, a hierarchical structure of class values is used in classification, where only promising paths in the hierarchy are considered.
