The goal of this project is to develop new algorithms for text categorization, text segmentation, and text summarization based upon a natural language processing technique called `information extraction`. Information extraction techniques provide a level of linguistic analysis that is not supported by word-based information retrieval systems, but are more robust and scalable than in-depth natural language processing techniques. Information extraction is particularly well-suited for text categorization because many categorization problems require the identification of role relationships and contextual distinctions that cannot be captured by keyword analysis. The project involves building a multi-faceted text categorization system that supports multiple text processing capabilities, including multi-class categorization, topic segmentation, and domain-specific text summarization. The objective is to achieve good performance on multiple text corpora and different category sets. This research represents a new approach to text categorization, for which many businesses and government agencies have critical applications including related tasks such as text routing and filtering.
