NDLI: Relational paraphrase acquisition from Wikipedia: The WRPA method and corpus

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Relational paraphrase acquisition from Wikipedia: The WRPA method and corpus

Content Provider	Scilit
Author	Vila, M. Rodríguez, H. Martí, M. A.
Copyright Year	2013
Description	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.
Related Links	https://www.cambridge.org/core/services/aop-cambridge-core/content/view/956CC67E9AAACC88B8EFE789832DCE00/S1351324913000235a.pdf/div-class-title-relational-paraphrase-acquisition-from-wikipedia-the-wrpa-method-and-corpus-a-href-fn01a-ref-type-fn-a-div.pdf
Ending Page	389
Page Count	35
Starting Page	355
ISSN	13513249
e-ISSN	14698110
DOI	10.1017/s1351324913000235
Journal	Natural Language Engineering
Issue Number	3
Volume Number	21
Language	English
Publisher	Cambridge University Press (CUP)
Publisher Date	2015-05-01
Access Restriction	Open
Subject Keyword	Natural Language Engineering Language Studies Relational Paraphrases Paraphrase Corpus Paraphrase Acquisition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Linguistics and Language Software

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