The aim of this workshop is to examine recent theoretical, methodological, and practical innovations from the various communities interested in text categorization. By analyzing the different underlying assumptions and state-of-the-art methodologies used in text categorization research, as well as successful applications of this work, we hope to foster new interactions between researchers in this area.

Particular areas of interest for the workshop include, but are not limited to:

• Text Representations: bag-of-words, n-grams, noun phrases, Latent Semantic Indexing, term weighting, parse trees, etc. Strengths and weaknesses of these representations for categorization. Novel representations.
• Classification Methods: Bayesian classifiers, first-order learning, neural networks, decision trees, support vector machines, Rocchio, etc. Novel classification methods well suited for text. Relative performance of classification methods. Challenges in terms of scaling/complexity.
• Clustering Methods: hierarchical and iterative methods, mixture models, similarity measures, etc. New methods that are useful for text. Evaluation metrics.
• Formal Models: Formal analysis of current methods. Analysis of the assumptions in various models of text and language. Advantages and disadvantages of these models. Methods for tractably enriching these models.
• Feature Selection: Feature selection/dimensionality reduction techniques that useful for text categorization. Issues of efficacy and efficiency.
• Data Transformation: Feature weighting methods. Discretization techniques. Natural language processing. Word sense disambiguation. Use of dictionaries and thesauri for text augmentation.
• Evaluation: Appropriate evaluation metrics. Significance tests. Experimental methodology. The use of "standard" text collections.
• Innovative Applications of Text Learning: Learning integrated into systems for information retrieval. Novel applications of text categorization. Fusion of text with multi-media information (audio, video, etc.) for categorization.

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