Expected answer type construction using analogical reasoning in a question answering task

Hapnes Toba, Mirna Adriani, Ruli Manurung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In a question answering system (QAS), question analysis component has an important task to determine the expected answer type (EAT) of a given question. Many QAS's rely their question analysis performance on manually developed patterns, such as in Open Ephyra (OE), one of a state of the art freely available QAS. Recently, there are a number of studies which investigated the influence of statistical relational framework to learn question-answer pairs in particular component of a QAS. In this study, we propose an approach that utilizes the intensity of statistical learning of question-answer pairs as a means to develop EAT patterns. In a question analysis experiment setting by using factoid testing questions from QA@CLEF 2008, our result outperforms the accuracy of manually constructed patterns of OE, with 84.17% against 81.67%.

Original languageEnglish
Title of host publicationICACSIS 2011 - 2011 International Conference on Advanced Computer Science and Information Systems, Proceedings
Pages283-289
Number of pages7
Publication statusPublished - 1 Dec 2011
Event2011 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2011 - Jakarta, Indonesia
Duration: 17 Dec 201118 Dec 2011

Publication series

NameICACSIS 2011 - 2011 International Conference on Advanced Computer Science and Information Systems, Proceedings

Conference

Conference2011 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2011
Country/TerritoryIndonesia
CityJakarta
Period17/12/1118/12/11

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