2009 AspectGuidedTextCategoriza

• (Roth & Tu, 2009) ⇒ Dan Roth, and Yuancheng Tu. (2009). “Aspect Guided Text Categorization with Unobserved Labels.” In: Proceedings of the Ninth IEEE International Conference on Data Mining (ICDM 2009). doi:10.1109/ICDM.2009.129

Subject Headings: Supervised Multiclass Classification Algorithm.

Quotes

Abstract

This paper proposes a novel multiclass classification method and exhibits its advantage in the domain of text categorization with a large label space and, most importantly, when some of the labels were not observed in the training data. The key insight is the introduction of intermediate aspect variables that encode properties of the labels. Aspect variables serve as a joint representation for observed and unobserved labels. This way the classification problem can be viewed as a structure learning problem with natural constraints on assignments to the aspect variables. We solve the problem as a constrained optimization problem over multiple learners and show significant improvement in classifying short sentences into a large label space of categories, including previously unobserved categories.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 AspectGuidedTextCategoriza	Dan Roth Yuancheng Tu			Aspect Guided Text Categorization with Unobserved Labels		ICDM 2009 Proceedings		10.1109/ICDM.2009.129		2009