NDLI: Semi-supervised single-label text categorization using centroid-based classifiers

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Semi-supervised single-label text categorization using centroid-based classifiers

Content Provider	ACM Digital Library
Author	Oliveira, Arlindo L. Cardoso-Cachopo, Ana
Abstract	In this paper we study the effect of using unlabeled data in conjunction with a small portion of labeled data on the accuracy of a centroid-based classifier used to perform single-label text categorization. We chose to use centroid-based methods because they are very fast when compared with other classification methods, but still present an accuracy close to that of the state-of-the-art methods. Efficiency is particularly important for very large domains, like regular news feeds, or the web. We propose the combination of Expectation-Maximization with a centroid-based method to incorporate information about the unlabeled data during the training phase. We also propose an alternative to EM, based on the incremental update of a centroid-based method with the unlabeled documents during the training phase. We show that these approaches can greatly improve accuracy relatively to a simple centroid-based method, in particular when there are very small amounts of labeled data available (as few as one single document per class). Using one synthetic and three real-world datasets, we show that, if the initial model of the data is sufficiently precise, using unlabeled data improves performance. On the other hand, using unlabeled data degrades performance if the initial model is not precise enough.
Starting Page	844
Ending Page	851
Page Count	8
File Format	PDF
ISBN	1595934804
DOI	10.1145/1244002.1244189
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-03-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Semi-supervised learning Single-label text categorization Centroid-based models Online learning
Content Type	Text
Resource Type	Article

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