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IJCNLP
2004
Springer

Combining Labeled and Unlabeled Data for Learning Cross-Document Structural Relationships

13 years 9 months ago
Combining Labeled and Unlabeled Data for Learning Cross-Document Structural Relationships
Multi-document discourse analysis has emerged with the potential of improving various NLP applications. Based on the newly proposed Cross-document Structure Theory (CST), this paper describes an empirical study that classifies CST relationships between sentence pairs extracted from topically related documents, exploiting both labeled and unlabeled data. We investigate a binary classifier for determining existence of structural relationships and a full classifier using the full taxonomy of relationships. We show that in both cases the exploitation of unlabeled data helps improve the performance of learned classifiers.
Zhu Zhang, Dragomir R. Radev
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where IJCNLP
Authors Zhu Zhang, Dragomir R. Radev
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