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        <title>Unsupervised Resource Creation for Textual Inference Applications</title>
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    <name type="personal">
        <namePart type="given">Jeremy</namePart>
        <namePart type="family">Bensley</namePart>
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            <title>Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)</title>
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        <name type="personal">
            <namePart type="given">Nicoletta</namePart>
            <namePart type="family">Calzolari</namePart>
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        <name type="personal">
            <namePart type="given">Khalid</namePart>
            <namePart type="family">Choukri</namePart>
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        <name type="personal">
            <namePart type="given">Bente</namePart>
            <namePart type="family">Maegaard</namePart>
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            <namePart type="given">Joseph</namePart>
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        <name type="personal">
            <namePart type="given">Jan</namePart>
            <namePart type="family">Odijk</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Stelios</namePart>
            <namePart type="family">Piperidis</namePart>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Tapias</namePart>
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            <publisher>European Language Resources Association (ELRA)</publisher>
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    <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.</abstract>
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%0 Conference Proceedings
%T Unsupervised Resource Creation for Textual Inference Applications
%A Bensley, Jeremy
%A Hickl, Andrew
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Tapias, Daniel
%S Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)
%D 2008
%8 May
%I European Language Resources Association (ELRA)
%C Marrakech, Morocco
%F bensley-hickl-2008-unsupervised
%X 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.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/146_paper.pdf

Markdown (Informal)

[Unsupervised Resource Creation for Textual Inference Applications](http://www.lrec-conf.org/proceedings/lrec2008/pdf/146_paper.pdf) (Bensley & Hickl, LREC 2008)

• Unsupervised Resource Creation for Textual Inference Applications (Bensley & Hickl, LREC 2008)

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).