Most text categorization methods require text content of documents that is often difficult to obtain. We consider "Collaborative Text Categorization", where each document is represented by the feedback from a large number of users. Our study focuses on the semisupervised case in which one key challenge is that a significant number of users have not rated any labeled document. To address this problem, we examine several semi-supervised learning methods and our empirical study shows that collaborative text categorization is more effective than content-based text categorization and the manifold regularization is more effective than other state-of-the-art semi-supervised learning methods. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Jin, R., Wu, M., & Sukthankar, R. (2007). Semi-supervised collaborative text classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4701 LNAI, pp. 600–607). Springer Verlag. https://doi.org/10.1007/978-3-540-74958-5_58
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