Learning Embedded Discourse Mechanisms for Information Extraction

Applying Machine Learning to Discourse Processing

Volume

Papers from the 1998 AAAI Spring Symposium

Contents

We address the problem of learning discourse-level merging strategies within the context of a natural language information extraction system. While we report on work currently in progress, results of preliminary experiments employing classification tree learning, maximum entropy modeling, and clustering methods are described. We also discuss motivations for moving away from supervised methods and toward unsupervised or weakly supervised methods.