Paper: Learning the Fine-Grained Information Status of Discourse Entities

ACL ID E12-1081
Title Learning the Fine-Grained Information Status of Discourse Entities
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

While information status (IS) plays a cru- cial role in discourse processing, there have only been a handful of attempts to automat- ically 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 experi- ments with a set of Switchboard dialogues, the learning-based approach achieves an ac- curacy of 78.7%, outperforming the rule- based approach by 21.3%.