Mistake-driven learning in text categorization

Ido Dagan, Yael Karov, Dan Roth

Research output: Contribution to conferencePaperpeer-review

111 Scopus citations


Learning problems in the text processing domain often map the text to a space whose dimensions are the measured features of the text, e.g., its words. Three characteristic properties of this domain are (a) very high dimensionality, (b) both the learned concepts and the instances reside very sparsely in the feature space, and (c) a high variation in the number of active features in an instance. In this work we study three mistake-driven learning algorithms for a typical task of this nature - text categorization. We argue that these algorithms- which categorize documents bY learning a linear separator in the feature space - have a few properties that make them ideal for this domain. We then show that a quantum leap in performance is achieved when we further modify the algorithms to better address some of the specific characteristics of the domain. In particular, we demonstrate (1) how variation in document length can be tolerated by either normalizing feature weights or by using negative weights, (2) the positive effect of applying a threshold range in training, (3) alternatives in considering feature frequency, and (4) the benefits of discarding features while training. Overall, we present an algorithm, a variation of Littlestone's Winnow, which performs significantly better than any other algorithm tested on this task using a similar feature set.

Original languageEnglish
Number of pages9
StatePublished - 1997
Event2nd Conference on Empirical Methods in Natural Language Processing, EMNLP 1997 - Providence, United States
Duration: 1 Aug 19972 Aug 1997


Conference2nd Conference on Empirical Methods in Natural Language Processing, EMNLP 1997
Country/TerritoryUnited States

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© Proceedings of the 2nd Conference on Empirical Methods in Natural Language Processing, EMNLP 1997. All rights reserved.


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