Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Computer Evaluation of Indexing and Text Processing
Journal of the ACM (JACM)
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Towards CST-enhanced summarization
Eighteenth national conference on Artificial intelligence
A Maximum-Entropy-Inspired Parser
A Maximum-Entropy-Inspired Parser
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Learning cross-document structural relationships using boosting
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Weakly supervised natural language learning without redundant views
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A common theory of information fusion from multiple text sources step one: cross-document structure
SIGDIAL '00 Proceedings of the 1st SIGdial workshop on Discourse and dialogue - Volume 10
Weakly-supervised relation classification for information extraction
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Statement map: assisting information crediblity analysis by visualizing arguments
Proceedings of the 3rd workshop on Information credibility on the web
Statement map: reducing web information credibility noise through opinion classification
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Revisiting Cross-document Structure Theory for multi-document discourse parsing
Information Processing and Management: an International Journal
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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.