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        <title>Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation</title>
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            <title>Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)</title>
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            <namePart type="given">Nicoletta</namePart>
            <namePart type="family">Calzolari</namePart>
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    <abstract>Data Selection has emerged as a common issue in language technologies. We define Data Selection as the choosing of a subset of training data that is most effective for a given task. This paper describes deductive feature detection, one component of a data selection system for machine translation. Feature detection determines whether features such as tense, number, and person are expressed in a language. The database of the The World Atlas of Language Structures provides a gold standard against which to evaluate feature detection. The discovered features can be used as input to a Navigator, which uses active learning to determine which piece of language data is the most important to acquire next.</abstract>
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%0 Conference Proceedings
%T Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation
%A Clark, Jonathan
%A Frederking, Robert
%A Levin, Lori
%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 clark-etal-2008-toward
%X Data Selection has emerged as a common issue in language technologies. We define Data Selection as the choosing of a subset of training data that is most effective for a given task. This paper describes deductive feature detection, one component of a data selection system for machine translation. Feature detection determines whether features such as tense, number, and person are expressed in a language. The database of the The World Atlas of Language Structures provides a gold standard against which to evaluate feature detection. The discovered features can be used as input to a Navigator, which uses active learning to determine which piece of language data is the most important to acquire next.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/308_paper.pdf

Markdown (Informal)

[Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation](http://www.lrec-conf.org/proceedings/lrec2008/pdf/308_paper.pdf) (Clark et al., LREC 2008)

• Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation (Clark et al., LREC 2008)

ACL

• Jonathan Clark, Robert Frederking, and Lori Levin. 2008. Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).