Learning for Text Categorization
Papers from the AAAI Workshop
Mehran Sahami, Chair
The enormous growth of on-line information has led to a comparable growth in the need for methods that help users organize such information. One area in particular that has seen much recent research activity is the use of automated learning techniques to categorize text documents. Such methods are useful for addressing problems including, but not limited to: keyword tagging, word sense disambiguation, information filtering and routing, sentence parsing, clustering of related documents and classification of documents into pre-defined topics.
The aim of this workshop was to examine recent theoretical, methodological, and practical innovations from the various communities interested in text categorization. The workshop covered recent advances from such fields as machine learning, Bayesian networks, information retrieval, natural language processing, case-based reasoning, language modeling and speech recognition. 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, the workshop tried to foster new interactions between researchers in this area.