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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Zhang, Z., & Radev, D. (2005). Combining labeled and unlabeled data for learning cross-document structural relationships. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3248, pp. 32–41). Springer Verlag. https://doi.org/10.1007/978-3-540-30211-7_4
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