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        <namePart type="given">Leila</namePart>
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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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            <namePart type="given">Bente</namePart>
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            <namePart type="given">Daniel</namePart>
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    <abstract>Although answering list questions is not a new research area, answering them automatically still remains a challenge. The median F-score of systems that participated in TREC 2007 Question Answering track is still very low (0.085) while 74% of the questions had a median F-score of 0. In this paper, we propose a novel approach to answering list questions. This approach is based on the hypothesis that answer instances of a list question co-occur in the documents and sentences related to the topic of the question. We use a clustering method to group the candidate answers that co-occur more often. To pinpoint the right cluster, we use the target and the question keywords as spies to return the cluster that contains these keywords.</abstract>
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%0 Conference Proceedings
%T Answering List Questions using Co-occurrence and Clustering
%A Razmara, Majid
%A Kosseim, Leila
%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 razmara-kosseim-2008-answering
%X Although answering list questions is not a new research area, answering them automatically still remains a challenge. The median F-score of systems that participated in TREC 2007 Question Answering track is still very low (0.085) while 74% of the questions had a median F-score of 0. In this paper, we propose a novel approach to answering list questions. This approach is based on the hypothesis that answer instances of a list question co-occur in the documents and sentences related to the topic of the question. We use a clustering method to group the candidate answers that co-occur more often. To pinpoint the right cluster, we use the target and the question keywords as spies to return the cluster that contains these keywords.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/814_paper.pdf

Markdown (Informal)

[Answering List Questions using Co-occurrence and Clustering](http://www.lrec-conf.org/proceedings/lrec2008/pdf/814_paper.pdf) (Razmara & Kosseim, LREC 2008)

• Answering List Questions using Co-occurrence and Clustering (Razmara & Kosseim, LREC 2008)

ACL

• Majid Razmara and Leila Kosseim. 2008. Answering List Questions using Co-occurrence and Clustering. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).