An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Sentence level discourse parsing using syntactic and lexical information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Representing discourse coherence: a corpus-based analysis
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Using phrasal patterns to identify discourse relations
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
SigDIAL '06 Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue
Automatic sense prediction for implicit discourse relations in text
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Recognizing implicit discourse relations in the Penn Discourse Treebank
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Sense annotation in the Penn discourse treebank
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Kernel based discourse relation recognition with temporal ordering information
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Predicting discourse connectives for implicit discourse relation recognition
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Agglomerative hierarchical clustering with constraints: theoretical and empirical results
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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Motivated by the critical importance of connectives in recognizing discourse relations, we present an unsupervised cross-argument inference mechanism to implicit discourse relation recognition. The basic idea is to infer the implicit discourse relation of an argument pair from a large number of comparable argument pairs, which are automatically retrieved from the web in an unsupervised way. In this way, the inference proceeds from explicit relations to implicit ones via connective as bridge. This kind of pair-to-pair inference is based on the assumption that two argument pairs with high content similarity (i.e. comparable argument pairs) should have similar discourse relationship. Evaluation on PDTB proves the effectiveness of our inference mechanism in implicit relation recognition to the four level-1 relations. It also shows that our mechanism significantly outperforms other alternatives.