Chapter

Computational Linguistics and Intelligent Text Processing

Volume 8403 of the series Lecture Notes in Computer Science pp 330-339

Statistical Relational Learning to Recognise Textual Entailment

  • Miguel RiosAffiliated withResearch Group in Computational Linguistics, University of Wolverhampton
  • , Lucia SpeciaAffiliated withDepartment of Computer Science, University of Sheffield
  • , Alexander GelbukhAffiliated withCentro de Investigación en Computación, Instituto Politécnico Nacional
  • , Ruslan MitkovAffiliated withResearch Group in Computational Linguistics, University of Wolverhampton

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Abstract

We propose a novel approach to recognise textual entailment (RTE) following a two-stage architecture – alignment and decision – where both stages are based on semantic representations. In the alignment stage the entailment candidate pairs are represented and aligned using predicate-argument structures. In the decision stage, a Markov Logic Network (MLN) is learnt using rich relational information from the alignment stage to predict an entailment decision. We evaluate this approach using the RTE Challenge datasets. It achieves the best results for the RTE-3 dataset and shows comparable performance against the state of the art approaches for other datasets.