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EDU-Based Similarity for Paraphrase Identification

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Natural Language Processing and Information Systems (NLDB 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7934))

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Abstract

We propose a new method to compute the similarity between two sentences based on elementary discourse units, EDU-based similarity. Unlike conventional methods, which directly compute similarities based on sentences, our method divides sentences into discourse units and uses them to compute similarities. We also show the relation between paraphrases and discourse units, which plays an important role in paraphrasing. We apply our method to the paraphrase identification task. By using only a single SVM classifier, we achieve 93.1% accuracy on the PAN corpus, a large corpus for detecting paraphrases.

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References

  1. Agirre, E., Cer, D., Diab, M., Gonzalez-Agirre, A.: SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity. In: Proceedings of SemEval, pp. 385–393 (2012)

    Google Scholar 

  2. Bach, N.X., Minh, N.L., Shimazu, A.: A Reranking Model for Discourse Segmentation using Subtree Features. In: Proceedings of SIGDIAL, pp. 160–168 (2012)

    Google Scholar 

  3. Bach, N.X., Le Minh, N., Shimazu, A.: UDRST: A Novel System for Unlabeled Discourse Parsing in the RST Framework. In: Isahara, H., Kanzaki, K. (eds.) JapTAL 2012. LNCS (LNAI), vol. 7614, pp. 250–261. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  4. Barzilay, R., McKeown, K.R., Elhadad, M.: Information Fusion in the Context of Multi-Document Summarization. In: Proceedings of ACL, pp. 550–557 (1999)

    Google Scholar 

  5. Bentivogli, L., Dagan, I., Dang, H.T., Giampiccolo, D., Magnini, B.: The fifth Pascal Recognizing Textual Entailment Challenge. In: Proceedings of TAC (2009)

    Google Scholar 

  6. Callison-Burch, C., Koehn, P., Osborne, M.: Improved Statistical Machine Translation Using Paraphrases. In: Proceedings of NAACL, pp. 17–24 (2006)

    Google Scholar 

  7. Carlson, L., Marcu, D., Okurowski, M.E.: RST Discourse Treebank. Linguistic Data Consortium (LDC) (2002)

    Google Scholar 

  8. Chan, Y.S., Ng, H.T.: MAXSIM: A Maximum Similarity Metric for Machine Translation Evaluation. In: Proceedings of ACL-HLT, pp. 55–62 (2008)

    Google Scholar 

  9. Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology 2(3), 27:1-27:27 (2011)

    Article  Google Scholar 

  10. Corley, C., Mihalcea, R.: Measuring the Semantic Similarity of Texts. In: Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, pp. 13–18 (2005)

    Google Scholar 

  11. Das, D., Smith, N.A.: Paraphrase Identification as Probabilistic Quasi-Synchronous Recognition. In: Proceedings of ACL-IJCNLP, pp. 468–476 (2009)

    Google Scholar 

  12. Denkowski, M., Lavie, M.: Extending the METEOR Machine Translation Metric to the Phrase Level. In: Proceedings of NAACL, pp. 250–253 (2010)

    Google Scholar 

  13. Doddington, G.: Automatic Evaluation of Machine Translation Quality using N-gram Co-occurrence Statistics. In: Proceedings of the 2nd International Conference on Human Language Technology Research, pp. 138–145 (2002)

    Google Scholar 

  14. Dolan, B., Quirk, C., Brockett, C.: Unsupervised Construction of Large Paraphrase Corpora: Exploiting Massively Parallel News Sources. In: Proceedings of COLING, pp. 350–356 (2004)

    Google Scholar 

  15. Duboue, P.A., Chu-Carroll, J.: Answering the Question You Wish They had Asked: The Impact of Paraphrasing for Question Answering. In: Proceedings of NAACL, pp. 33–36 (2006)

    Google Scholar 

  16. Fernando, S., Stevenson, M.: A Semantic Similarity Approach to Paraphrase Detection. In: Proceedings of CLUK (2008)

    Google Scholar 

  17. Finch, A., Hwang, Y.S., Sumita, E.: Using Machine Translation Evaluation Techniques to Determine Sentence-level Semantic Equivalence. In: Proceedings of the 3rd International Workshop on Paraphrasing, pp. 17–24 (2005)

    Google Scholar 

  18. Habash, N., Kholy, A.E.: SEPIA: Surface Span Extension to Syntactic Dependency Precision-based MT Evaluation. In: Proceedings of the Workshop on Metrics for Machine Translation at AMTA (2008)

    Google Scholar 

  19. Hernault, H., Bollegala, D., Ishizuka, M.: A Sequential Model for Discourse Segmentation. In: Gelbukh, A. (ed.) CICLing 2010. LNCS, vol. 6008, pp. 315–326. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  20. Klein, D., Manning, C.: Accurate Unlexicalized Parsing. In: Proceedings of ACL, pp. 423–430 (2003)

    Google Scholar 

  21. Kozareva, Z., Montoyo, A.: Paraphrase Identification on the Basis of Supervised Machine Learning Techniques. In: Salakoski, T., Ginter, F., Pyysalo, S., Pahikkala, T. (eds.) FinTAL 2006. LNCS (LNAI), vol. 4139, pp. 524–533. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  22. Leusch, G., Ueffing, N., Ney, H.: A Novel String-to-String Distance Measure with Applications to Machine Translation Evaluation. In: Proceedings of MT Summit IX (2003)

    Google Scholar 

  23. Madnani, N., Tetreault, J., Chodorow, M.: Re-examining Machine Translation Metrics for Paraphrase Identification. In: Proceedings of NAACL-HLT, pp. 182–190 (2012)

    Google Scholar 

  24. Mann, W.C., Thompson, S.A.: Rhetorical Structure Theory. Toward a Functional Theory of Text Organization. Text 8, 243–281 (1988)

    Google Scholar 

  25. Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and Knowledge-based Measures of Text Semantic Similarity. In: Proceedings of AAAI, pp. 775–780 (2006)

    Google Scholar 

  26. Niessen, S., Och, F.J., Leusch, G., Ney., H.: An Evaluation Tool for Machine Translation: Fast Evaluation for MT Research. In: Proceedings of LREC (2000)

    Google Scholar 

  27. Parker, S.: BADGER: A New Machine Translation Metric. In: Proceedings of the Workshop on Metrics for Machine Translation at AMTA (2008)

    Google Scholar 

  28. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: A Method for Automatic Evaluation of Machine Translation. In: Proceedings of ACL, pp. 311–318 (2002)

    Google Scholar 

  29. Regneri, M., Wang, R.: Using Discourse Information for Paraphrase Extraction. In: Proceedings of EMNLP-CONLL, pp. 916–927 (2012)

    Google Scholar 

  30. Rus, V., McCarthy, P.M., Lintean, M.C., McNamara, D.S., Graesser, A.C.: Paraphrase Identification with Lexico-Syntactic Graph Subsumption. In: Proceedings of FLAIRS Conference, pp. 201–206 (2008)

    Google Scholar 

  31. Snover, M., Dorr, B., Schwartz, R., Micciulla, L., Makhoul, J.: A Study of Translation Edit Rate with Targeted Human Annotation. In: Proceedings of the Conference of the Association for Machine Translation in the Americas, AMTA (2006)

    Google Scholar 

  32. Snover, M., Madnani, N., Dorr, B., Schwartz, R.: TER-Plus: Paraphrase, Semantic, and Alignment Enhancements to Translation Edit Rate. Machine Translation 23(23), 117–127 (2009)

    Article  Google Scholar 

  33. Socher, R., Huang, E.H., Pennington, J., Ng, A.Y., Manning, C.D.: Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection. In: Advances in Neural Information Processing Systems 24 (NIPS), pp. 801–809 (2011)

    Google Scholar 

  34. Uzuner, O., Katz, B., Nahnsen, T.: Using Syntactic Information to Identify Plagiarism. In: Proceedings of the 2nd Workshop on Building Educational Applications using Natural Language Processing, pp. 37–44 (2005)

    Google Scholar 

  35. Vapnik, V.N.: Statistical Learning Theory. Wiley Interscience (1998)

    Google Scholar 

  36. Wan, S., Dras, R., Dale, M., Paris, C.: Using Dependency-Based Features to Take the “Para-farce” out of Paraphrase. In: Proceedings of the 2006 Australasian Language Technology Workshop, pp. 131–138 (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan

    Ngo Xuan Bach, Nguyen Le Minh & Akira Shimazu

Authors
  1. Ngo Xuan Bach
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  2. Nguyen Le Minh
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  3. Akira Shimazu
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Editor information

Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, 2 rue Conté, 75003, Paris, France

    Elisabeth Métais

  2. School of Computing, Science and Engineering, University of Salford, The Crescent, M5 4WT, Salford, Lancashire, UK

    Farid Meziane  & Sunil Vadera  & 

  3. School of Computing Science and Engineering, University of Salford, The Crescent, M5 4WT, Salford, Lancashire, UK

    Mohamad Saraee

  4. Department of Decision and Information Sciences School of Business Administration, Oakland University, 306 Elliott Hall, 48309, Rochester, MI, USA

    Vijayan Sugumaran

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Bach, N.X., Le Minh, N., Shimazu, A. (2013). EDU-Based Similarity for Paraphrase Identification. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2013. Lecture Notes in Computer Science, vol 7934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38824-8_6

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  • DOI: https://doi.org/10.1007/978-3-642-38824-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38823-1

  • Online ISBN: 978-3-642-38824-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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