1999 UsingMaximumEntropyforTextClass

• (Nigam et al., 1999) ⇒ Kamal Nigam, John Lafferty, and Andrew McCallum. (1999). “Using Maximum Entropy for Text Classification.” In: IJCAI-99 workshop on machine learning for information filtering.

Subject Headings: Maximum Entropy Algorithm; Text Classification Algorithm.

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• http://scholar.google.com/scholar?q=%22Using+maximum+entropy+for+text+classification%22+1999

Quotes

Abstract

This paper proposes the use of maximum entropy techniques for text classification. Maximum entropy is a probability distribution estimation technique widely used for a variety of natural language tasks, such as language modeling, part-of-speech tagging, and text segmentation. The underlying principle of maximum entropy is that without external knowledge, one should prefer distributions that are uniform. Constraints on the distribution, derived from labeled training data, inform the technique where to be minimally non-uniform. The maximum entropy formulation has a unique solution which can be found by the improved iterative scaling algorithm. In this paper, maximum entropy is used for text classification by estimating the conditional distribution of the class variable given the document. In experiments on several text datasets we compare accuracy to naive Bayes and show that maximum entropy is sometimes significantly better, but also sometimes worse. Much future work remains, but the results indicate that maximum entropy is a promising technique for text classification.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1999 UsingMaximumEntropyforTextClass	Kamal Nigam John D. Lafferty Andrew McCallum			Using Maximum Entropy for Text Classification						1999