Learning textual entailment from examples

In this paper we present a novel approach for learning entailment relations from positive and negative examples. We define a similarity between two text-hypothesis pairs based on a syntactic and lexical information. We experimented our model within the RTE 2006 challenge obtaining the accuracy of 63.88% and 62.50% for the two submissions.

[1]  David W. Conrath,et al.  Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.

[2]  Bernard Haasdonk,et al.  Feature space interpretation of SVMs with indefinite kernels , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Lauri Karttunen,et al.  Local Textual Inference: Can it be Defined or Circumscribed? , 2005, EMSEE@ACL.

[4]  Ido Dagan,et al.  Web Based Probabilistic Textual Entailment , 2005 .

[5]  Michael Collins,et al.  New Ranking Algorithms for Parsing and Tagging: Kernels over Discrete Structures, and the Voted Perceptron , 2002, ACL.

[6]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[7]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[8]  L. Ferro,et al.  MITRE ’ s Submissions to the EU Pascal RTE Challenge , 2005 .

[9]  Eugene Charniak,et al.  A Maximum-Entropy-Inspired Parser , 2000, ANLP.

[10]  John A. Carroll,et al.  Applied morphological processing of English , 2001, Natural Language Engineering.

[11]  Rada Mihalcea,et al.  Measuring the Semantic Similarity of Texts , 2005, EMSEE@ACL.

[12]  Jean-Philippe Tarel,et al.  Non-Mercer Kernels for SVM Object Recognition , 2004, BMVC.

[13]  Alessandro Moschitti,et al.  A Study on Convolution Kernels for Shallow Statistic Parsing , 2004, ACL.

[14]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[15]  Ted Pedersen,et al.  WordNet::Similarity - Measuring the Relatedness of Concepts , 2004, NAACL.