Unsupervised Resource Creation for Textual Inference Applications

Jeremy Bensley, Andrew Hickl


Abstract
This paper explores how a battery of unsupervised techniques can be used in order to create large, high-quality corpora for textual inference applications, such as systems for recognizing textual entailment (TE) and textual contradiction (TC). We show that it is possible to automatically generate sets of positive and negative instances of textual entailment and contradiction from textual corpora with greater than 90% precision. We describe how we generated more than 1 million TE pairs - and a corresponding set of and 500,000 TC pairs - from the documents found in the 2 GB AQUAINT-2 newswire corpus.
Anthology ID:
L08-1224
Volume:
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)
Month:
May
Year:
2008
Address:
Marrakech, Morocco
Editors:
Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2008/pdf/146_paper.pdf
DOI:
Bibkey:
Cite (ACL):
Jeremy Bensley and Andrew Hickl. 2008. Unsupervised Resource Creation for Textual Inference Applications. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).
Cite (Informal):
Unsupervised Resource Creation for Textual Inference Applications (Bensley & Hickl, LREC 2008)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2008/pdf/146_paper.pdf