Abstract
The corpora available for training discourse relation classifiers are annotated using a general set of discourse relations. However, for certain applications, custom discourse relations are required. Creating a new annotated corpus with a new relation taxonomy is a time-consuming and costly process. We address this problem by proposing a semi-supervised approach to discourse relation classification based on Structural Learning. First, we solve a set of auxiliary classification problems using unlabeled data. Second, the learned classifiers are used to extend feature vectors to train a discourse relation classifier. By defining a relevant set of auxiliary classification problems, we show that the proposed method brings improvement of at least 50% in accuracy and F-score on the RST Discourse Treebank and Penn Discourse Treebank, when small training sets of ca. 1000 training instances are employed. This is an attractive perspective for training discourse relation classifiers on domains where little amount of labeled training data is available.
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References
Louis, A., Joshi, A., Nenkova, A.: Discourse indicators for content selection in summarization. In: Proc. of SIGDIAL 2010, pp. 147–156 (2010)
Piwek, P., Hernault, H., Prendinger, H., Ishizuka, M.: Generating dialogues between virtual agents automatically from text. In: Pelachaud, C., Martin, J.-C., André, E., Chollet, G., Karpouzis, K., Pelé, D. (eds.) IVA 2007. LNCS (LNAI), vol. 4722, pp. 161–174. Springer, Heidelberg (2007)
Carlson, L., Marcu, D., Okurowski, M.E.: Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory. In: Proc. of Second SIGdial Workshop on Discourse and Dialogue, vol. 16, pp. 1–10 (2001)
Wolf, F., Gibson, E.: Representing discourse coherence: A corpus-based study. Computational Linguistics 31, 249–287 (2005)
Prasad, R., Dinesh, N., Lee, A., Miltsakaki, E., Robaldo, L., Joshi, A., Webber, B.: The Penn Discourse TreeBank 2.0. In: Proc. of LREC 2008 (2008)
Mann, W.C., Thompson, S.A.: Rhetorical Structure Theory: Toward a functional theory of text organization. Text 8, 243–281 (1988)
Georg, G., Hernault, H., Cavazza, M., Prendinger, H., Ishizuka, M.: From rhetorical structures to document structure: Shallow pragmatic analysis for document engineering. In: Proc. of DocEng 2009, pp. 185–192. ACM, New York (2009)
Ando, R.K., Zhang, T.: A framework for learning predictive structures from multiple tasks and unlabeled data. J. Mach. Learn. Res. 6, 1817–1853 (2005)
Hernault, H., Bollegala, D., Ishizuka, M.: A semi-supervised approach to improve classification of infrequent discourse relations using feature vector extension. In: Proc. of EMNLP 2010, pp. 399–409 (2010)
Marcu, D., Echihabi, A.: An unsupervised approach to recognizing discourse relations. In: Proc. of ACL 2002, pp. 368–375 (2002)
Soricut, R., Marcu, D.: Sentence level discourse parsing using syntactic and lexical information. In: Proc. of NA-ACL 2003, vol. 1, pp. 149–156 (2003)
duVerle, D.A., Prendinger, H.: A novel discourse parser based on Support Vector Machine classification. In: Proc. of ACL 2009, pp. 665–673 (2009)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Sagae, K.: Analysis of discourse structure with syntactic dependencies and data-driven shift-reduce parsing. In: Proc. of IWPT 2009, pp. 81–84 (2009)
Pitler, E., Raghupathy, M., Mehta, H., Nenkova, A., Lee, A., Joshi, A.: Easily identifiable discourse relations. In: Proc. of COLING 2008 (Posters), pp. 87–90 (2008)
Pitler, E., Louis, A., Nenkova, A.: Automatic sense prediction for implicit discourse relations in text. In: Proc. of ACL 2009, pp. 683–691 (2009)
Lin, Z., Kan, M.Y., Ng, H.T.: Recognizing implicit discourse relations in the Penn Discourse Treebank. In: Proc. of EMNLP 2009, pp. 343–351 (2009)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proc. of COLT 1998, pp. 92–100 (1998)
Caruana, R.: Multitask learning: A knowledge-based source of inductive bias. In: Proc. of ICML 1993, pp. 41–48 (1993)
Plackett, R.L.: Karl Pearson and the chi-squared test. International Statistical Review / Revue Internationale de Statistique 51, 59–72 (1983)
Blitzer, J., McDonald, R., Pereira, F.: Domain adaptation with structural correspondence learning. In: Proc. of EMNLP 2006, pp. 120–128 (2006)
Loper, E., Bird, S.: NLTK: The natural language toolkit. In: Proc. of ACL 2002 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pp. 63–70 (2002)
Magerman, D.M.: Statistical decision-tree models for parsing. In: Proc. of ACL 1995, pp. 276–283 (1995)
Klein, D., Manning, C.D.: Fast exact inference with a factored model for natural language parsing. In: Advances in Neural Information Processing Systems, vol. 15. MIT Press, Cambridge (2003)
Prasad, R., Miltsakaki, E., Dinesh, N., Lee, A., Joshi, A., Robaldo, L., Webber, B.: The Penn Discourse Treebank 2.0 annotation manual. Technical report, University of Pennsylvania Institute for Research in Cognitive Science (2008)
Marcus, M.P., Marcinkiewicz, M.A., Santorini, B.: Building a large annotatedcorpus of English: The Penn Treebank. Computational Linguistics 19, 313–330 (1993)
Okazaki, N.: Classias: A collection of machine-learning algorithms for classification (2009), http://www.chokkan.org/software/classias/
Zhou, Z.M., Xu, Y., Niu, Z.Y., Lan, M., Su, J., Tan, C.L.: Predicting discourse connectives for implicit discourse relation recognition. In: Proc. of COLING 2010 (Posters), pp. 1507–1514 (2010)
Louis, A., Joshi, A., Prasad, R., Nenkova, A.: Using entity features to classify implicit discourse relations. In: Proc. of the SIGDIAL 2010, pp. 59–62 (2010)
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Hernault, H., Bollegala, D., Ishizuka, M. (2011). Semi-supervised Discourse Relation Classification with Structural Learning. In: Gelbukh, A.F. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19400-9_27
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DOI: https://doi.org/10.1007/978-3-642-19400-9_27
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