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Educational Question Answering based on Social Media Content

Gurevych, Iryna ; Bernhard, Delphine ; Ignatova, Kateryna ; Toprak, Cigdem
Dimitrova, Vania ; Mizoguchi, Riichiro ; du Boulay, Benedict ; Graesser, Art (eds.) (2009):
Educational Question Answering based on Social Media Content.
In: Frontiers in Artificial Intelligence and Applications, 200, In: Proceedings of the 14th International Conference on Artificial Intelligence in Education. Building learning systems that care: From knowledge representation to affective modelling (AIED2009), pp. 133-140, IOS Press, [Book Section]

Abstract

We analyze the requirements for an educational Question Answering (QA) system operating on social media content. As a result, we identify a set of advanced natural language processing (NLP) technologies to address the challenges in educational QA. We conducted an inter-annotator agreement study on subjective question classification in the Yahoo!Answers social Q&A site and propose a simple, but effective approach to automatically identify subjective questions. We also developed a two-stage QA architecture for answering learners’ questions. In the first step, we aim at re-using human answers to already answered questions by employing question paraphrase identification [1]. In the second step, we apply information retrieval techniques to perform answer retrieval from social media content. We show that elaborate techniques for question preprocessing are crucial.

Item Type: Book Section
Erschienen: 2009
Editors: Dimitrova, Vania ; Mizoguchi, Riichiro ; du Boulay, Benedict ; Graesser, Art
Creators: Gurevych, Iryna ; Bernhard, Delphine ; Ignatova, Kateryna ; Toprak, Cigdem
Title: Educational Question Answering based on Social Media Content
Language: English
Abstract:

We analyze the requirements for an educational Question Answering (QA) system operating on social media content. As a result, we identify a set of advanced natural language processing (NLP) technologies to address the challenges in educational QA. We conducted an inter-annotator agreement study on subjective question classification in the Yahoo!Answers social Q&A site and propose a simple, but effective approach to automatically identify subjective questions. We also developed a two-stage QA architecture for answering learners’ questions. In the first step, we aim at re-using human answers to already answered questions by employing question paraphrase identification [1]. In the second step, we apply information retrieval techniques to perform answer retrieval from social media content. We show that elaborate techniques for question preprocessing are crucial.

Title of Book: Proceedings of the 14th International Conference on Artificial Intelligence in Education. Building learning systems that care: From knowledge representation to affective modelling (AIED2009)
Series Name: Frontiers in Artificial Intelligence and Applications
Volume: 200
Publisher: IOS Press
Uncontrolled Keywords: Educational Natural Language Processing;UKP_a_ENLP;UKP_p_QAEL
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 31 Dec 2016 14:29
Official URL: http://ebooks.iospress.nl/volumearticle/5146
Identification Number: TUD-CS-2009-0015
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