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A probabilistic graphical model for joint answer ranking in question answering

Authors:

Luo SiAuthors Info & Claims

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 343 - 350

https://doi.org/10.1145/1277741.1277801

Published: 23 July 2007 Publication History

Abstract

Graphical models have been applied to various information retrieval and natural language processing tasks in the recent literature. In this paper, we apply a probabilistic graphical model for answer ranking in question answering. This model estimates the joint probability of correctness of all answer candidates, from which the probability of correctness of an individual candidate can be inferred. The joint prediction model can estimate both the correctness of individual answers as well as their correlations, which enables a list of accurate and comprehensive answers. This model was compared with a logistic regression model which directly estimates the probability of correctness of each individual answer candidate. An extensive set of empirical results based on TREC questions demonstrates the effectiveness of the joint model for answer ranking. Furthermore, we combine the joint model with the logistic regression model to improve the efficiency and accuracy of answer ranking.

References

[1]

J. Allan, C. Wade, and A. Bolivar. Retrieval and novelty detection at the sentence level. In Proceedings of SIGIR, 2003.

Digital Library

[2]

D. Buscaldi and P. Rosso. Mining Knowledge from Wikipedia for the Question Answering task. In Proceedings of the International Conference on Language Resources and Evaluation, 2006.

[3]

J. Chu-Carroll, K. Czuba, J. Prager, and A. Ittycheriah. In question answering, two heads are better than one. In Proceedings of HLT/NAACL, 2003.

Digital Library

[4]

C. Clarke, G. Cormack, and T. Lynam. Exploiting redundancy in question answering. In Proceedings of SIGIR, 2001.

Digital Library

[5]

R. G. Cowell, A. P. Dawid, S. L. Lauritzen, and D. J. Spiegelhalter. Probabilistic Networks and Expert Systems. Springer, 1999.

Digital Library

[6]

A. Echihabi, U. Hermjakob, E. Hovy, D. Marcu, E. Melz, and D. Ravichandran. How to select an answer string? In T. Strzalkowski and S. Harabagiu, editors, Advances in Textual Question Answering. Kluwer, 2004.

[7]

C. Fellbaum. WordNet: An Electronic Lexical Database. MIT Press, 1998.

[8]

S. Harabagiu, D. Moldovan, C. Clark, M. Bowden, J. Williams, and J. Bensley. Answer mining by combining extraction techniques with abductive reasoning. In Proceedings of TREC, 2003.

[9]

S. Harabagiu, D. Moldovan, M. Pasca, R. Mihalcea, M. Surdeanu, R. Bunsecu, R. Girju, V. Rus, and P. Morarescu. Falcon: Boosting knowledge for answer engines. In Proceedings of TREC, 2000.

[10]

G. Hinton and T. Sejnowski. Learning and relearning in Boltzmann machines. In Rumelhart, editor, Parallel Distributed Processing, pages pp. 282--317. MIT Press, 1986.

Digital Library

[11]

V. Jijkoun, J. van Rantwijk, D. Ahn, E. T. K. Sang, and M. de Rijke. The University of Amsterdam at CLEFυQA 2006. In Working Notes CLEF, 2006.

[12]

M. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. Saul. An introduction to variational methods for graphical models. In M. Jordan, editor, Learning in Graphical Models. Kluwer Academic Publishers, Boston, 1998.

Digital Library

[13]

B. Katz, J. J. Lin, D. Loreto, W. Hildebrandt, M. Bilotti, S. Felshin, A. Fernandes, G. Marton, and F. Mora. Integrating web-based and corpus-based techniques for question answering. In TREC, pages 426--435, 2003.

[14]

J. Ko, L. Si, and E. Nyberg. A Probabilistic Framework for Answer Selection in Question Answering. Proceedings of NAACL/HLT, 2007.

[15]

B. Magnini, M. Negri, R. Pervete, and H. Tanev. Comparing statistical and content-based techniques for answer validation on the web. In Proceedings of the VIII Convegno AI*IA, 2002.

[16]

T. Minka. A Comparison of Numerical Optimizers for Logistic Regression. Unpublished draft, 2003.

[17]

D. Moldovan, D. Clark, S. Harabagiu, and S. Maiorano. Cogex: A logic prover for question answering. In Proceedings of HLT-NAACL, 2003.

Digital Library

[18]

E. Nyberg, T. Mitamura, R. Frederking, M. Bilotti, K. Hannan, L. Hiyakumoto, J. Ko, F. Lin, V. Pedro, and A. Schlaikjer. JAVELIN I and II Systems at TREC 2005. In Proceedings of Text REtrieval Conference, 2005.

[19]

E. Nyberg, T. Mitamura, R. Frederking, V. Pedro, M. Bilotti, A. Schlaikjer, and K. Hannan. Extending the javelin qa system with domain semantics. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI), 2005.

[20]

J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2000.

Digital Library

[21]

J. Prager, E. Brown, A. Coden, and D. Radev. Question answering by predictive annotation. In Proceedings of SIGIR, 2000.

Digital Library

[22]

J. Prager, J. Chu-Carroll, K. Czuba, C. Welty, A. Ittycheriah, and R. Mahindru. IBM's PIQUANT in TREC2003. In Proceedings of TREC, 2003.

[23]

S. Schlobach, M. Olsthoorn, and M. de Rijke. Type checking in open-domain question answering. In Proceedings of European Conference on Artificial Intelligence, 2004.

[24]

E. Voorhees. Overview of the TREC 2002 question answering track. In Proceedings of Text REtrieval Conference, 2002.

[25]

E. Voorhees. Overview of the TREC 2003 question answering track. In Proceedings of Text REtrieval Conference, 2003.

[26]

J. Xu, A. Licuanan, J. May, S. Miller, and R. Weischedel. TREC 2002 QA at BBN: Answer Selection and Confidence Estimation. In Proceedings of Text REtrieval Conference, 2002.

[27]

H. Yang, H. Cui, M. Maslennikov, L. Qiu, M.-Y. Kan, and T.-S. Chua. QUALIFIER In TREC-12 QA Main Task. In TREC, pages 480--488, 2003.

Cited By

Moulton MNg Y(2024)Boolean interpretation, matching, and ranking of natural language queries in product selection systemsDiscover Computing10.1007/s10791-024-09432-x27:1Online publication date: 3-Apr-2024
https://doi.org/10.1007/s10791-024-09432-x
Xu BZhuge H(2020)The influence of semantic link network on the ability of question-answering systemFuture Generation Computer Systems10.1016/j.future.2020.02.042108(1-14)Online publication date: Jul-2020
https://doi.org/10.1016/j.future.2020.02.042
Xie WWong LLee LAu OHao T(2020)A Semantic Expansion-Based Joint Model for Answer Ranking in Chinese Question Answering SystemsInformation Retrieval Technology10.1007/978-3-030-42835-8_3(22-33)Online publication date: 27-Feb-2020
https://doi.org/10.1007/978-3-030-42835-8_3
Show More Cited By

Index Terms

A probabilistic graphical model for joint answer ranking in question answering
1. Information systems
  1. Information retrieval

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Information & Contributors

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Published In

cover image ACM Conferences

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

July 2007

946 pages

ISBN:9781595935977

DOI:10.1145/1277741

General Chairs:
Wessel Kraaij
TNO, The Netherlands
,
Arjen P. de Vries
CWI, The Netherlands
,
Program Chairs:
Charles L. A. Clarke
University of Waterloo, Canada
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Noriko Kando
National Institute of Informatics, Japan

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 23 July 2007

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SIGIR07

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SIGIR07: The 30th Annual International SIGIR Conference

July 23 - 27, 2007

Amsterdam, The Netherlands

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

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Cited By

Moulton MNg Y(2024)Boolean interpretation, matching, and ranking of natural language queries in product selection systemsDiscover Computing10.1007/s10791-024-09432-x27:1Online publication date: 3-Apr-2024
https://doi.org/10.1007/s10791-024-09432-x
Xu BZhuge H(2020)The influence of semantic link network on the ability of question-answering systemFuture Generation Computer Systems10.1016/j.future.2020.02.042108(1-14)Online publication date: Jul-2020
https://doi.org/10.1016/j.future.2020.02.042
Xie WWong LLee LAu OHao T(2020)A Semantic Expansion-Based Joint Model for Answer Ranking in Chinese Question Answering SystemsInformation Retrieval Technology10.1007/978-3-030-42835-8_3(22-33)Online publication date: 27-Feb-2020
https://doi.org/10.1007/978-3-030-42835-8_3
Husain OSalim NAlias RAbdelsalam SHassan A(2019)Expert Finding Systems: A Systematic ReviewApplied Sciences10.3390/app92042509:20(4250)Online publication date: 11-Oct-2019
https://doi.org/10.3390/app9204250
Xu DShi YTsang IOng YGong CShen X(2019)Survey on Multi-Output LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2019.2945133(1-21)Online publication date: 2019
https://doi.org/10.1109/TNNLS.2019.2945133
Daskalakis CDikkala NKamath G(2019)Testing Ising ModelsIEEE Transactions on Information Theory10.1109/TIT.2019.293225565:11(6829-6852)Online publication date: Nov-2019
https://doi.org/10.1109/TIT.2019.2932255
Zhu NZhang ZMa H(2019)Ranking answers of comparative questions using heterogeneous information organization from social mediaSignal, Image and Video Processing10.1007/s11760-019-01465-w13:7(1267-1274)Online publication date: 25-Mar-2019
https://doi.org/10.1007/s11760-019-01465-w
Daskalakis CDikkala NKamath GCzumaj A(2018)Testing ising modelsProceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms10.5555/3174304.3175435(1989-2007)Online publication date: 7-Jan-2018
https://dl.acm.org/doi/10.5555/3174304.3175435
Xu BWang XZhuge H(2018)Using Abstraction Level in Question Answering System2018 14th International Conference on Semantics, Knowledge and Grids (SKG)10.1109/SKG.2018.00040(253-256)Online publication date: Sep-2018
https://doi.org/10.1109/SKG.2018.00040
Nicula BRuseti SRebedea T(2018)Improving Deep Learning for Multiple Choice Question Answering with Candidate ContextsAdvances in Information Retrieval10.1007/978-3-319-76941-7_62(678-683)Online publication date: 1-Mar-2018
https://doi.org/10.1007/978-3-319-76941-7_62
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