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Probabilistic model for definitional question answering

Published: 06 August 2006 Publication History

Abstract

This paper proposes a probabilistic model for definitional question answering (QA) that reflects the characteristics of the definitional question. The intention of the definitional question is to request the definition about the question target. Therefore, an answer for the definitional question should contain the content relevant to the topic of the target, and have a representation form of the definition style. Modeling the problem of definitional QA from both the topic and definition viewpoints, the proposed probabilistic model converts the task of answering the definitional questions into that of estimating the three language models: topic language model, definition language model, and general language model. The proposed model systematically combines several evidences in a probabilistic framework. Experimental results show that a definitional QA system based on the proposed probabilistic model is comparable to state-of-the-art systems.

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    cover image ACM Conferences
    SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
    August 2006
    768 pages
    ISBN:1595933697
    DOI:10.1145/1148170
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    Publication History

    Published: 06 August 2006

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    Author Tags

    1. definitional question answering
    2. language model
    3. probabilistic model

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    SIGIR06: The 29th Annual International SIGIR Conference
    August 6 - 11, 2006
    Washington, Seattle, USA

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    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    • (2020)A study on different closed domain question answering approachesInternational Journal of Speech Technology10.1007/s10772-020-09692-0Online publication date: 11-Mar-2020
    • (2020)Definitional Question Answering Using Text TripletsData Engineering and Communication Technology10.1007/978-981-15-1097-7_10(119-130)Online publication date: 9-Jan-2020
    • (2019)MOQASJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-18136436:4(3495-3512)Online publication date: 1-Jan-2019
    • (2017)Ontology-based sentence extraction for answering why-question2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)10.1109/EECSI.2017.8239127(1-6)Online publication date: Sep-2017
    • (2015)Learning to Rank Answers for Definitional Question AnsweringChinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data10.1007/978-3-319-25816-4_26(326-332)Online publication date: 8-Nov-2015
    • (2013)Identifying Authoritative and Reliable Contents in Community Question Answering with Domain KnowledgeRevised Selected Papers of PAKDD 2013 International Workshops on Trends and Applications in Knowledge Discovery and Data Mining - Volume 786710.1007/978-3-642-40319-4_12(133-142)Online publication date: 14-Apr-2013
    • (2012)Can click patterns across user's query logs predict answers to definition questions?Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics10.5555/2380816.2380831(99-108)Online publication date: 23-Apr-2012
    • (2012)CONTEXTUAL LANGUAGE MODELS FOR RANKING ANSWERS TO NATURAL LANGUAGE DEFINITION QUESTIONSComputational Intelligence10.1111/j.1467-8640.2012.00426.x28:4(528-548)Online publication date: 3-May-2012
    • (2012)Leveraging the network information for evaluating answer quality in a collaborative question answering portalSocial Network Analysis and Mining10.1007/s13278-011-0046-42:3(197-215)Online publication date: 11-Jan-2012
    • (2011)Informative sentence retrieval for domain specific terminologiesProceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I10.5555/2025756.2025787(242-252)Online publication date: 28-Jun-2011
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