Generalizing sub-sentential paraphrase acquisition across original signal type of text pairs

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

  • Affiliations:
  • LIMSI-CNRS & Univ. Paris Sud, Orsay, France;LIMSI-CNRS & Univ. Paris Sud, Orsay, France;LIMSI-CNRS & Univ. Paris Sud, Orsay, France

  • Venue:
  • EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
  • Year:
  • 2012

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Abstract

This paper describes a study on the impact of the original signal (text, speech, visual scene, event) of a text pair on the task of both manual and automatic sub-sentential paraphrase acquisition. A corpus of 2,500 annotated sentences in English and French is described, and performance on this corpus is reported for an efficient system combination exploiting a large set of features for paraphrase recognition. A detailed quantified typology of sub-sentential paraphrases found in our corpus types is given.