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Learning the Fine-Grained Information Status of Discourse Entities
| Content Provider | CiteSeerX |
|---|---|
| Author | Rahman, Altaf Ng, Vincent |
| Abstract | 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%. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Rich Knowledge Source Discourse Entity Cru-cial Role Fine-grained Determination Fine-grained Information Status Switchboard Dialogue Information Status Learning-based Approach Discourse Processing Rule-based Approach |
| Content Type | Text |
