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        <title>Scaling Answer Type Detection to Large Hierarchies</title>
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        <namePart type="given">Kirk</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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            <namePart type="given">Khalid</namePart>
            <namePart type="family">Choukri</namePart>
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            <namePart type="given">Bente</namePart>
            <namePart type="family">Maegaard</namePart>
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            <namePart type="given">Joseph</namePart>
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            <namePart type="given">Jan</namePart>
            <namePart type="family">Odijk</namePart>
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        <name type="personal">
            <namePart type="given">Stelios</namePart>
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            <namePart type="given">Daniel</namePart>
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    <abstract>This paper describes the creation of a state-of-the-art answer type detection system capable of recognizing more than 200 different expected answer types with greater than 85% precision and recall. After describing how we constructed a new, multi-tiered answer type hierarchy from the set of entity types recognized by Language Computer Corporation’s CICEROLITE named entity recognition system, we describe how we used this hierarchy to annotate a new corpus of more than 10,000 English factoid questions. We show how an answer type detection system trained on this corpus can be used to enhance the accuracy of a state-of-the-art question-answering system (Hickl et al., 2007; Hickl et al., 2006b) by more than 7% overall.</abstract>
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        <url>https://aclanthology.org/L08-1137/</url>
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%0 Conference Proceedings
%T Scaling Answer Type Detection to Large Hierarchies
%A Roberts, Kirk
%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 roberts-hickl-2008-scaling
%X This paper describes the creation of a state-of-the-art answer type detection system capable of recognizing more than 200 different expected answer types with greater than 85% precision and recall. After describing how we constructed a new, multi-tiered answer type hierarchy from the set of entity types recognized by Language Computer Corporation’s CICEROLITE named entity recognition system, we describe how we used this hierarchy to annotate a new corpus of more than 10,000 English factoid questions. We show how an answer type detection system trained on this corpus can be used to enhance the accuracy of a state-of-the-art question-answering system (Hickl et al., 2007; Hickl et al., 2006b) by more than 7% overall.
%U https://aclanthology.org/L08-1137/

Markdown (Informal)

[Scaling Answer Type Detection to Large Hierarchies](https://aclanthology.org/L08-1137/) (Roberts & Hickl, LREC 2008)

• Scaling Answer Type Detection to Large Hierarchies (Roberts & Hickl, LREC 2008)

ACL

• Kirk Roberts and Andrew Hickl. 2008. Scaling Answer Type Detection to Large Hierarchies. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).