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.

• Type: Conference
• Year: 2004
• Where: IJCNLP
• Authors: Zhu Zhang, Dragomir R. Radev
• Keywords: CST Relationships, IJCNLP 2004, Multi-document Discourse Analysis, Unlabeled Data