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
Automatic detection of causal relations for Question Answering
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Using automatically labelled examples to classify rhetorical relations: An assessment
Natural Language Engineering
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
Annotating attribution in the Penn Discourse TreeBank
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Using syntax to disambiguate explicit discourse connectives in text
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
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
Probabilistic head-driven parsing for discourse structure
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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Implicit discourse relation recognition is difficult due to the absence of explicit discourse connectives between arbitrary spans of text. In this paper, we use language models to predict the discourse connectives between the arguments pair. We present two methods to apply the predicted connectives to implicit discourse relation recognition. One is to use the sense frequency of the specific connectives in a supervised framework. The other is to directly use the presence of the predicted connectives in an unsupervised way. Results on PDTB2 show that using language model to predict the connectives can achieve comparable F-scores to the previous state-of-art method. Our method is quite promising in that not only it has a very small number of features but also once a language model based on other resources is trained it can be more adaptive to other languages and domains.