skip to main content
10.1145/1076034.1076101acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
Article

Generic soft pattern models for definitional question answering

Published: 15 August 2005 Publication History

Abstract

This paper explores probabilistic lexico-syntactic pattern matching, also known as soft pattern matching. While previous methods in soft pattern matching are ad hoc in computing the degree of match, we propose two formal matching models: one based on bigrams and the other on the Profile Hidden Markov Model (PHMM). Both models provide a theoretically sound method to model pattern matching as a probabilistic process that generates token sequences. We demonstrate the effectiveness of these models on definition sentence retrieval for definitional question answering. We show that both models significantly outperform state-of-the-art manually constructed patterns. A critical difference between the two models is that the PHMM technique handles language variations more effectively but requires more training data to converge. We believe that both models can be extended to other areas where lexico-syntactic pattern matching can be applied.

References

[1]
S. Blair-Goldensohn, K.R. McKeown and A. Hazen Schlaikjer, A Hybrid Approach for QA Track Definitional Questions, Proc. of TREC 2003, 2003, pp. 336--343.
[2]
H. Cui, M.-Y. Kan and T.-S. Chua, Unsupervised Learning of Soft Patterns for Generating Definitions from Online News, Proc. of WWW '04, New York, 2004, pp. 90--99.
[3]
H. Cui, M.-Y. Kan, T.-S. Chua and J. Xiao, A Comparative Study on Sentence Retrieval for Definitional Question Answering, SIGIR Workshop on Information Retrieval for Question Answering (IR4QA), Sheffield, U.K., 2004.
[4]
A.P. Dempster, N.M. Laird and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, 39:1--38, 1977.
[5]
S. Harabagiu, D. Moldovan, C. Clark, M. Bowden, J. Williams and J. Bensley, Answer Mining by Combining Extraction Techniques with Abductive Reasoning, Proc. of TREC 2003, 2003.
[6]
W. Hildebrandt, B. Katz and J. Lin, Answering Definition Questions with Multiple Knowledge Sources, Proc. of HLT/NAACL 2004, Boston, MA, 2004, pp. 49--56.
[7]
F. Jelinek and R. L. Mercer, Interpolated estimation of markov source parameters from sparse data, Proc. of the Workshop Pattern Recognition in Practice, Amsterdam, Holland, 1980, pp. 381--397.
[8]
A. Krogh, M. Brown, I.S. Mian K. Sjolander and D. Haussler, Hidden Markov Models in Computational Biology - Applications to Protein Modeling, J. Mol. Biol. (1994) 235, pp. 1501--1531.
[9]
C.-Y. Lin and E.H. Hovy, Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics, Proc. of HLT-NAACL '03, Edmonton, Canada, 2003, pp. 71--78.
[10]
C.D. Manning and H. Schtze, editors. Foundations of Statistical Natural Language Processing, The MIT Press, Cambridge, MA, 1999.
[11]
I. Muslea, Extraction patterns for information extraction tasks: A survey, Proc. of AAAI-99 Workshop on Machine Learning for Information Extraction, 1999, pp.1--6.
[12]
D. Ravichandran and E. Hovy, Learning Surface Text Patterns for a Question Answering System, Proc. of ACL '02, Philadelphia, July 2002, pp. 41--47.
[13]
E. Riloff, and J. Wiebe, Learning Extraction Patterns for Subjective Expressions, Proc. of EMNLP '03, 2003.
[14]
R. Rosenfeld, Two decades of statistical language modeling: Where do we go from here, Proc. of the IEEE, 88, August, 2000, pp. 1270--1278.
[15]
M. Skounakis, M. Craven, and S. Ray, Hierarchical hidden markov models for information extraction, Proc. of IJCAI '03, 2003.
[16]
E.M.Voorhees, Overview of the TREC 2003 question answering track, Proc. of TREC 2003, 2003.
[17]
E.M. Voorhees, Overview of the TREC 2004 question answering track, Proc. of TREC 2004, 2004.
[18]
J. Xiao, T.-S. Chua and H. Cui, Cascading Use of Soft and Hard Matching Pattern Rules for Weakly Supervised Information Extraction, Proc. of COLING '04, Geneva, Switzerland, 2004, pp.542--548.
[19]
J. Xu, R. M. Weischedel and A. Licuanan, Evaluation of an extraction-based approach to answering definitional questions, Proc. of SIGIR '04, Sheffield, UK, 2004, pp. 418--424.
[20]
H. Yang, H. Cui, M.-Y. Kan, M. Maslennikov, L. Qiu and T.-S. Chua, QUALIFIER in TREC 12 QA Main Task, Proc. of TREC 2003, 2003, pp. 54--63.

Cited By

View all
  • (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
  • (2017)Similarity-based Distant Supervision for Definition RetrievalProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133032(527-536)Online publication date: 6-Nov-2017
  • (2015)Learning to Rank Short Text Pairs with Convolutional Deep Neural NetworksProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767738(373-382)Online publication date: 9-Aug-2015
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
August 2005
708 pages
ISBN:1595930345
DOI:10.1145/1076034
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 August 2005

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. definitional question answering
  2. probabilistic models
  3. soft pattern

Qualifiers

  • Article

Conference

SIGIR05
Sponsor:

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (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
  • (2017)Similarity-based Distant Supervision for Definition RetrievalProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133032(527-536)Online publication date: 6-Nov-2017
  • (2015)Learning to Rank Short Text Pairs with Convolutional Deep Neural NetworksProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767738(373-382)Online publication date: 9-Aug-2015
  • (2015)Bridging the vocabulary gap between questions and answer sentencesInformation Processing and Management: an International Journal10.1016/j.ipm.2015.04.00551:5(595-615)Online publication date: 1-Sep-2015
  • (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
  • (2014)Mining Semi-Structured Online Knowledge Bases to Answer Natural Language Questions on Community QA WebsitesProceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2661968(341-350)Online publication date: 3-Nov-2014
  • (2014)Question AnsweringNatural Language Processing of Semitic Languages10.1007/978-3-642-45358-8_11(335-370)Online publication date: 25-Mar-2014
  • (2014)Applying Dependency Relations to Definition ExtractionNatural Language Processing and Information Systems10.1007/978-3-319-07983-7_10(63-74)Online publication date: 2014
  • (2012)Structural relationships for large-scale learning of answer re-rankingProceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval10.1145/2348283.2348383(741-750)Online publication date: 12-Aug-2012
  • (2012)Toward Automated Definition Acquisition From Operations LawIEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews10.1109/TSMCC.2011.211064342:2(223-232)Online publication date: 1-Mar-2012
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media