Learning the Fine-Grained Information Status of Discourse Entities

Abstract

While information status (IS) plays a crucial role in discourse processing, there have only been a handful of attempts to automatically determine the IS of discourse entities. We examine a related but more challenging task, fine-grained IS determination, which involves classifying a discourse entity as one of 16 IS subtypes. We investigate the use of rich knowledge sources for this task in combination with a rule-based approach and a learning-based approach. In experiments with a set of Switchboard dialogues, the learning-based approach achieves an accuracy of 78.7%, outperforming the rule-based approach by 21.3%.

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