Encoding tree pair-based graphs in learning algorithms: The textual entailment recognition case

Alessandro Moschitti, Fabio Massimo Zanzotto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we provide a statistical machine learning representation of textual entailment via syntactic graphs constituted by tree pairs. We show that the natural way of representing the syntactic relations between text and hypothesis consists in the huge feature space of all possible syntactic tree fragment pairs, which can only be managed using kernel methods. Experiments with Support Vector Machines and our new kernels for paired trees show the validity of our interpretation.

Original languageEnglish
Title of host publicationProceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing, TextGraphs 2008
PublisherAssociation for Computational Linguistics and Chinese Language Processing
Pages25-32
Number of pages8
ISBN (Electronic)9781905593576
Publication statusPublished - 1 Jan 2008
Event3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing, TextGraphs 2008 - Manchester, United Kingdom
Duration: 24 Aug 2008 → …

Publication series

NameProceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing, TextGraphs 2008

Conference

Conference3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing, TextGraphs 2008
CountryUnited Kingdom
CityManchester
Period24/8/08 → …

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ASJC Scopus subject areas

  • Software

Cite this

Moschitti, A., & Zanzotto, F. M. (2008). Encoding tree pair-based graphs in learning algorithms: The textual entailment recognition case. In Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing, TextGraphs 2008 (pp. 25-32). (Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing, TextGraphs 2008). Association for Computational Linguistics and Chinese Language Processing.