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Answer Formulation for Question-Answering

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Advances in Artificial Intelligence (Canadian AI 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2671))

Abstract

In this paper, we describe our experimentations in evaluating answer formulation for question-answering (QA) systems. In the context of QA, answer formulation can serve two purposes: improving answer extraction or improving human-computer interaction (HCI). Each purpose has di.erent precision/recall requirements. We present our experiments for both purposes and argue that formulations of better grammatical quality are beneficial for both answer extraction and HCI.

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© 2003 Springer-Verlag Berlin Heidelberg

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Kosseim, L., Plamondon, L., Guillemette, L.J. (2003). Answer Formulation for Question-Answering. In: Xiang, Y., Chaib-draa, B. (eds) Advances in Artificial Intelligence. Canadian AI 2003. Lecture Notes in Computer Science, vol 2671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44886-1_5

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  • DOI: https://doi.org/10.1007/3-540-44886-1_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40300-5

  • Online ISBN: 978-3-540-44886-0

  • eBook Packages: Springer Book Archive

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