Machine Learning for Text Classification and Information Organization
ASIST Annual Meeting
Sunday, November 4, 2001, 9am-1pm
This timely half-day workshop will show you how to use machine learning successfully to categorize, filter, mine, and otherwise impose sense on
online data. By automating not just the classification of text, but also the production of the classification rules themselves, machine learning can make classification practical in situations where purely manual indexing would be
too slow or costly.
You Will Learn About
- The major approaches to training text classification systems
- Combining automated systems with manual classification in the most economical fashion
- Evaluating commercial text classification offerings
- Embedding text classification in larger systems
- Making "build vs. buy" decisions
David Lewis, independent consultant. David advises clients on the development and effective use of technology for classifying, mining, and retrieving text, as well as related areas of natural language
processing, machine learning, and bioinformatics. His clients have included companies of all sizes, as well as universities and research consortia. Prior to launching his consulting practice, he was a researcher for eight years at
Bell Labs and AT&T Labs, and before that a faculty member at the University of Chicago. He has authored over 40 technical papers on information retrieval and machine learning, has five patents, and has been extensively involved
in the design of U.S. government evaluations of information retrieval and natural language processing technology.