Learning Embedded Discourse Mechanisms for Information Extraction

Andrew Kehler

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.

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