Machine Learning in Information Access
Papers from the AAAI Spring Symposium
Marti A. Hearst & Haym Hirsh, Cochairs
As the volume and importance of the information available on the Internet continues to increase, there is a growing interest in information access in all areas of computer science. There has been substantial recent work on the application of machine learning techniques (e.g., inductive learning, genetic algorithms, and neural networks) to information access problems. For example, machine learning has been used to improve weights on terms for relevance feedback, to learn rules for filtering netnews articles, for automatic identification of hypertext links, and for text topic identification. Thus far, though, there has been no professional gathering devoted to investigating the use of machine learning techniques to improve access to textual information. The goal of this symposium was to provide the much-needed opportunity to develop new ideas and a well-defined community in this growing field. For this symposium, authors were asked to submit papers concerning the use of machine learning to enable or improve users' access to online information. Machine learning techniques are especially appropriate for, but not limited to, the following information access tasks: Text categorization and segmentation, routing/filtering, relevance, feedback, clustering, user preferences/usage pattern analysis, browsing, and multi-source integration.