Abstract
Textual Entailment recognition is a very difficult task as it is one of the fundamental problems in any semantic theory of natural language. As in many other NLP tasks, Machine Learning may offer important tools to better understand the problem. In this paper, we will investigate the usefulness of Machine Learning algorithms to address an apparently simple and well defined classification problem: the recognition of Textual Entailment. Due to its specificity, we propose an original feature space, the distance feature space, where we model the distance between the elements of the candidate entailment pairs. The method has been tested on the data of the Recognizing Textual Entailment (RTE) Challenge.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chierchia, G., McConnell-Ginet, S.: Meaning and Grammar: An introduction to Semantics. MIT Press, Cambridge (2001)
Dagan, I., Glickman, O.: Probabilistic textual entailment: Generic applied modeling of language variability. In: Proceedings of the Workshop on Learning Methods for Text Understanding and Mining, Grenoble, France (2004)
Basili, R., Moschitti, A., Pazienza, M.T.: Empirical investigation of fast text categorization over linguistic features. In: Proceedings of the 15th European Conference on Artificial Intelligence (ECAI 2002), Lyon, France (2002)
Joachims, T.: Learning to Classify Text using Support Vector Machines: Methods, Theory, and Algorithms. Kluwer Academic Publishers, Dordrecht (2002)
Glickman, O., Dagan, I.: A probabilistic setting and lexical coocurrence model for textual entailment. In: Proceedings of the ACL-Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor, Michigan (2005)
Corley, C., Mihalcea, R.: Measuring the semantic similarity of texts. In: Proceedings of the ACL-Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor, Michigan (2005)
Dagan, I., Glickman, O., Magnini, B.: The pascal recognising textual entailment challenge. In: PASCAL Challenges Workshop, Southampton, UK (2005)
Miller, G.A.: WordNet: A lexical database for English. Communications of the ACM 38, 39–41 (1995)
Resnik, P.: Using information content to evaluate semantic similarity. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, Canada (1995)
Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, Madison, WI (1998)
Vanderwende, L., Coughlin, D., Dolan, B.: What syntax can contribute in entailment task. In: Proceedings of the 1st Pascal Challenge Workshop, Southampton, UK (2005)
Pazienza, M.T., Pennacchiotti, M., Zanzotto, F.M.: A linguistic inspection of textual entailment. In: Bandini, S., Manzoni, S. (eds.) AI*IA 2005. LNCS (LNAI), vol. 3673, pp. 315–326. Springer, Heidelberg (2005)
Raina, R., Haghighi, A., Cox, C., Finkel, J., Michels, J., Toutanova, K., MacCartney, B., de Marneffe, M.C., Manning, C.D., Ng, A.Y.: Robust textual inference using diverse knowledge sources. In: Proceedings of the 1st Pascal Challenge Workshop, Southampton, UK (2005)
Kouylekov, M., Magnini, B.: Tree edit distance for textual entailment. In: Proceedings of the International Conference Recent Advances of Natural Language Processing (RANLP 2005), Borovets, Bulgaria (2005)
Lin, D.: Dependency-based evaluation of minipar. In: Proceedings of theWorkshop on Evaluation of Parsing Systems at LREC 1998, Granada, Spain (1998)
Proceedings of the Seventh Message Understanding Conference (MUC-7), Virginia USA. Morgan Kaufmann, San Francisco (1998)
Joachims, T.: Making large-scale svm learning practical. In: Schlkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods-Support Vector Learning. MIT Press, Cambridge (1999)
Collins, M., Duffy, N.: New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In: Proceedings of the ACL 2002, Philadelphia, PA (2002)
Moschitti, A.: A study on convolution kernels for shallow semantic parsing. In: Proceedings of the ACL 2004, Barcellona, Spain (2004)
Lin, D., Pantel, P.: DIRT, discovery of inference rules from text. In: Knowledge Discovery and Data Mining, pp. 323–328 (2001)
Harris, Z.: Distributional structure. In: Katz, J. (ed.) The Philosophy of Linguistics. Oxford University Press, New York (1985)
Glickman, O., Dagan, I.: Identifying lexical paraphrases from a single corpus: A case study for verbs. In: Proceedings of the International Conference Recent Advances of Natural Language Processing (RANLP 2003), Borovets, Bulgaria (2003)
Shearer, K., Bunke, H., Venkatesh, S., Kieronska, D.: Efficient graph mathicng for video indexing. Technical Report 1997, Department of Computer Science, Curtin University (1997)
Cho, C., Kim, J.: Recognizing 3-d objects by forward checking constrained tree search. PRL 13, 587–597 (1992)
Borner, K., Pippig, E., Tammer, E.C., Coulon, C.H.: Structural similarity and adaptation. In: Smith, I., Faltings, B.V. (eds.) EWCBR 1996. LNCS, vol. 1168, pp. 58–75. Springer, Heidelberg (1996)
Sanders, K.E., Kettler, B.P., Hendler, J.: The case for graph-structured representations. In: Proceedings of the Second International Conference on Case-based Reasoning, pp. 245–254. Springer, Heidelberg (1997)
Bunke, H.: Graph matching: Theoretical foundations, algorithms, and applications. In: Vision Interface 2000, Montreal, pp. 82–88. Springer, Heidelberg (2000)
Bunke, H., Shearer, K.: A graph distance metric based on the maximal common subgraph. Pattern Recogn. Lett. 19, 255–259 (1998)
Basili, R., Zanzotto, F.M.: Parsing engineering and empirical robustness. Natural Language Engineering 8(2-3) (2002)
Pazienza, M.T., Pennacchiotti, M., Zanzotto, F.M.: Identifying relational concept lexicalisations by using general linguistic knowledge. In: ECAI, pp. 1071–1072 (2004)
Wu, D.: Stochastic inversion transduction grammars, with application to segmentation, bracketing, and alignment of parallel corpora. Computational Linguistics 23, 207–223 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pazienza, M.T., Pennacchiotti, M., Zanzotto, F.M. (2006). Learning Textual Entailment on a Distance Feature Space. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds) Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. MLCW 2005. Lecture Notes in Computer Science(), vol 3944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11736790_14
Download citation
DOI: https://doi.org/10.1007/11736790_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33427-9
Online ISBN: 978-3-540-33428-6
eBook Packages: Computer ScienceComputer Science (R0)