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Relational paraphrase acquisition from Wikipedia: The WRPA method and corpus

Published online by Cambridge University Press:  16 September 2013

M. VILA
Affiliation:
CLiC, Departament de Lingüística, Universitat de Barcelona, Gran Via de les Corts Catalanes 585, 08007 Barcelona, Spain e-mail: marta.vila@ub.edu, amarti@ub.edu
H. RODRÍGUEZ
Affiliation:
TALP, Departament de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Jordi Girona Salgado 1-3, 08034 Barcelona, Spain e-mail: horacio@lsi.upc.edu
M. A. MARTÍ
Affiliation:
CLiC, Departament de Lingüística, Universitat de Barcelona, Gran Via de les Corts Catalanes 585, 08007 Barcelona, Spain e-mail: marta.vila@ub.edu, amarti@ub.edu

Abstract

Paraphrase corpora are an essential but scarce resource in Natural Language Processing. In this paper, we present the Wikipedia-based Relational Paraphrase Acquisition (WRPA) method, which extracts relational paraphrases from Wikipedia, and the derived WRPA paraphrase corpus. The WRPA corpus currently covers person-related and authorship relations in English and Spanish, respectively, suggesting that, given adequate Wikipedia coverage, our method is independent of the language and the relation addressed. WRPA extracts entity pairs from structured information in Wikipedia applying distant learning and, based on the distributional hypothesis, uses them as anchor points for candidate paraphrase extraction from the free text in the body of Wikipedia articles. Focussing on relational paraphrasing and taking advantage of Wikipedia-structured information allows for an automatic and consistent evaluation of the results. The WRPA corpus characteristics distinguish it from other types of corpora that rely on string similarity or transformation operations. WRPA relies on distributional similarity and is the result of the free use of language outside any reformulation framework. Validation results show a high precision for the corpus.

Type
Articles
Copyright
Copyright © Cambridge University Press 2013 

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Footnotes

This work was supported by the MINECO projects DIANA (TIN2012-38603-C02-02) and SKATER (TIN2012-38584-C06-01), as well as a MECD FPU grant (AP2008-02185). Also, we are grateful to Esther Arias, Santiago González, Rita Zaragoza and Oriol Borrega, the linguists that worked on the annotation processes.

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