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A discourse containing one or more sentences describes daily issues and events for people to communicate their thoughts and opinions.
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The Penn Discourse TreeBank 2.0. In Proceedings of the International Conference on Language Resources and Evaluation (Marrakech, Morocco) (LREC ’08) . European Language Resources Association, Paris, France, 1–8
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Recognizing Implicit Discourse Relations in the Penn Discourse Treebank. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (Singapore) (EMNLP ’09) . The Association for Computer Linguistics, Stroudsburg, PA, USA, 343–351
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Automatic sense prediction for implicit discourse relations in text. In Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP (Singapore) (ACL ’09) . The Association for Computer Linguistics, Stroudsburg, PA, USA, 683–691
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Supervised and unsupervised methods in employing discourse relations for improving opinion polarity classification. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (Singapore) (EMNLP ’09) . The Association for Computer Linguistics, Stroudsburg, PA, USA, 170–179
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Modelling relational data using bayesian clustered tensor factorization. In Advances in neural information processing systems (Vancouver, British Columbia, Canada) (NeurIPS ’09) . MIT Press, Cambridge, MA, USA, 1821–1828
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A semi-supervised approach to improve classification of infrequent discourse relations using feature vector extension. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (Massachusetts, USA) (EMNLP ’10) . The Association for Computational Linguistics, Stroudsburg, PA, USA, 399–409
Hugo Hernault, Danushka Bollegala, and Mitsuru Ishizuka. 2010 · 2010
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Using entity features to classify implicit discourse relations. In Proceedings of the SIGDIAL 2010 Conference (Tokyo, Japan) (SIGDIAL ’10) . The Association for Computer Linguistics, Stroudsburg, PA, USA, 59–62
Annie Louis, Aravind Joshi, Rashmi Prasad, and Ani Nenkova. 2010b · 2010
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Kernel Based Discourse Relation Recognition with Temporal Ordering Information. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (Uppsala, Sweden) (ACL ’10) . The Association for Computer Linguistics, Stroudsburg, PA, USA, 710–719
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The effects of discourse connectives prediction on implicit discourse relation recognition. In Proceedings of the SIGDIAL 2010 Conference (Tokyo, Japan) (SIGDIAL ’10) . The Association for Computer Linguistics, Stroudsburg, PA, USA, 139–146
Zhi Min Zhou, Man Lan, Zheng-Yu Niu, Yu Xu, and Jian Su. 2010a · 2010
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Cross-argument inference for implicit discourse relation recognition. In Proceedings of the 21st ACM international conference on Information and knowledge management (Maui, HI, USA) (CIKM ’12) . ACMPress, New York, NY, USA, 295–304
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A latent factor model for highly multi-relational data. In Advances in neural information processing systems (Lake Tahoe, Nevada, USA) (NeurIPS ’12) . MIT Press, Cambridge, MA, USA, 3167–3175
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Using Sense-labeled Discourse Connectives for Statistical Machine Translation. In Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra) (Avignon, France) (EACL ’12) . The Association for Computational Linguistics, Stroudsburg, PA, USA, 129–138
Thomas Meyer and Andrei Popescu-Belis. 2012 · 2012
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Improving implicit discourse relation recognition through feature set optimization. In Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue (Seoul, South Korea) (SIGDIAL ’12) . The Association for Computer Linguistics, Stroudsburg, PA, USA, 108–112
Joonsuk Park and Claire Cardie. 2012 · 2012
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Implicit discourse relation recognition by selecting typical training examples. In Proceedings of COLING 2012, the 23th International Conference on Computational Linguistics: Technical Papers (Mumbai, India) (COLING ’12) . The Association for Computational Linguistics, Stroudsburg, PA, USA, 2757–2772
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Yu Xu, Man Lan, Yue Lu, Zheng Yu Niu, and Chew Lim Tan. 2012 · 2012
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Aggregated Word Pair Features for Implicit Discourse Relation Disambiguation. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (Sofia, Bulgaria) (ACL ’13) . The Association for Computer Linguistics, Stroudsburg, PA, USA, 69–73
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Leveraging synthetic discourse data via multi-task learning for implicit discourse relation recognition. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Sofia, Bulgaria) (ACL ’13) . The Association for Computational Linguistics, Stroudsburg, PA, USA, 476–485
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Implicitation of discourse connectives in (machine) translation. In Proceedings of the Workshop on Discourse in Machine Translation (Sofia, Bulgaria) (ACL ’13) . The Association for Computational Linguistics, Stroudsburg, PA, USA, 19–26
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