acl acl2012 acl2012-184 acl2012-184-reference knowledge-graph by maker-knowledge-mining
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Author: Fan Bu ; Hang Li ; Xiaoyan Zhu
Abstract: Learning for sentence re-writing is a fundamental task in natural language processing and information retrieval. In this paper, we propose a new class of kernel functions, referred to as string re-writing kernel, to address the problem. A string re-writing kernel measures the similarity between two pairs of strings, each pair representing re-writing of a string. It can capture the lexical and structural similarity between two pairs of sentences without the need of constructing syntactic trees. We further propose an instance of string rewriting kernel which can be computed efficiently. Experimental results on benchmark datasets show that our method can achieve better results than state-of-the-art methods on two sentence re-writing learning tasks: paraphrase identification and recognizing textual entailment.
Baldrige, J. , Morton, T. and Bierner G. OpenNLP. http://opennlp.sourceforge.net/. Barzilay, R. and Lee, L. 2003. Learning to paraphrase: An unsupervised approach using multiple-sequence alignment. Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 16–23. Basilico, J. and Hofmann, T. 2004. Unifying collaborative and content-based filtering. Proceedings of the twenty-first international conference on Machine learning, pp. 9, 2004. Ben-Hur, A. and Noble, W.S. 2005. Kernel methods for predicting protein–protein interactions. Bioinformatics, vol. 21, pp. i38–i46, Oxford Univ Press. Bhagat, R. and Ravichandran, D. 2008. Large scale acquisition of paraphrases for learning surface patterns. Proceedings of ACL-08: HLT, pp. 674–682. Chang, C. and Lin, C. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology vol. 2, issue 3, pp. 27: 1– 27:27. Software available at http : / /www .cs ie . ntu .edu .tw/ ˜ c j l in/ l ibsvm Das, D. and Smith, N.A. 2009. Paraphrase identification as probabilistic quasi-synchronous recognition. 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, pp. 468–476. de Marneffe, M., MacCartney, B., Grenager, T., Cer, D., Rafferty A. and Manning C.D. 2006. Learning to distinguish valid textual entailments. Proc. of the Second PASCAL Challenges Workshop. Dolan, W.B. and Brockett, C. 2005. Automatically constructing a corpus of sentential paraphrases. Proc. of IWP. Giampiccolo, D., Magnini B., Dagan I., and Dolan B., editors 2007. The third pascal recognizing textual entailment challenge. Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pp. 1–9. Harmeling, S. 2007. An extensible probabilistic transformation-based approach to the third recognizing textual entailment challenge. Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pp. 137–142, 2007. Heilman, M. and Smith, N.A. 2010. Tree edit models for recognizing textual entailments, paraphrases, and answers to questions. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 1011-1019. Kashima, H. , Oyama, S. , Yamanishi, Y. and Tsuda, K. 2009. On pairwise kernels: An efficient alternative and generalization analysis. Advances in Knowledge 457 2009, Discovery and Data Mining, pp. 1030-1037, Springer. Kimeldorf, G. and Wahba, G. 1971 . Some results on Tchebycheffian spline functions. Journal of Mathematical Analysis and Applications, Vol.33, No. 1, pp.8295, Elsevier. Lin, D. and Pantel, P. 2001. DIRT-discovery of inference rules from text. Proc. of ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Lintean, M. and Rus, V. 2011. Dissimilarity Kernels for Paraphrase Identification. Twenty-Fourth International FLAIRS Conference. Leslie, C. , Eskin, E. and Noble, W.S. 2002. The spectrum kernel: a string kernel for SVM protein classification. Pacific symposium on biocomputing vol. 575, pp. 564-575, Hawaii, USA. Leslie, C. and Kuang, R. 2004. Fast string kernels using inexact matching for protein sequences. The Journal of Machine Learning Research vol. 5, pp. 1435-1455. Lodhi, H. , Saunders, C. , Shawe-Taylor, J. , Cristianini, N. and Watkins, C. 2002. Text classification using string kernels. The Journal of Machine Learning Research vol. 2, pp. 419-444. MacCartney, B. and Manning, C.D. 2008. Modeling semantic containment and exclusion in natural language inference. Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 521528, 2008. Moschitti, A. and Zanzotto, F.M. 2007. Fast and Effective Kernels for Relational Learning from Texts. Proceedings of the 24th Annual International Conference on Machine Learning, Corvallis, OR, USA, 2007. Qiu, L. and Kan, M.Y. and Chua, T.S. 2006. Paraphrase recognition via dissimilarity significance classification. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 18–26. Quirk, C. , Brockett, C. and Dolan, W. 2004. Monolingual machine translation for paraphrase generation. Proceedings ofEMNLP 2004, pp. 142-149, Barcelona, Spain. Sch o¨lkopf, B. and Smola, A.J. 2002. Learning with kernels: Support vector machines, regularization, optimization, and beyond. The MIT Press, Cambridge, MA. Vapnik, V.N. 2000. The nature of statistical learning theory. Springer Verlag. Wan, S. , Dras, M. , Dale, R. and Paris, C. 2006. Using dependency-based features to take the “Para-farce ” out of paraphrase. Proc. of the Australasian Language Technology Workshop, pp. 131–138. Zanzotto, F.M. , Pennacchiotti, M. and Moschitti, A. 2007. Shallow semantics in fast textual entailment rule learners. Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing, pp. 72–77. Zhang, Y. and Patrick, J. 2005. Paraphrase identification by text canonicalization. Proceedings of the Australasian Language Technology Workshop, pp. 160– 166. 458