Multitechnique paraphrase alignment: A contribution to pinpointing sub-sentential paraphrases

  • Authors:
  • Houda Bouamor;Auréelien Max;Anne Vilnat

  • Affiliations:
  • LIMSI-CNRS & University Paris Sud, Orsay Cedex, France;LIMSI-CNRS & University Paris Sud, Orsay Cedex, France;LIMSI-CNRS & University Paris Sud, Orsay Cedex, France

  • Venue:
  • ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
  • Year:
  • 2013

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Abstract

This work uses parallel monolingual corpora for a detailed study of the task of sub-sentential paraphrase acquisition. We argue that the scarcity of this type of resource is compensated by the fact that it is the most suited type for studies on paraphrasing. We propose a large exploration of this task with experiments on two languages with five different acquisition techniques, selected for their complementarity, their combinations, as well as four monolingual corpus types of varying comparability. We report, under all conditions, a significant improvement over all techniques by validating candidate paraphrases using a maximum entropy classifier. An important result of our study is the identification of difficult-to-acquire paraphrase pairs, which are classified and quantified in a bilingual typology.