column

A Language Modeling Approach to Information Retrieval

Authors:

Jay M. Ponte,

W. Bruce CroftAuthors Info & Claims

ACM SIGIR Forum, Volume 51, Issue 2

Pages 202 - 208

https://doi.org/10.1145/3130348.3130368

Published: 02 August 2017 Publication History

Get Access

References

[1]

Bookstein, A. and D. Swanson. "Probabilistic models for automatic indexing" Journal for the American Society for Information Science, v.25 no.5 pp. 312-318, 1976.

Crossref

Google Scholar

[2]

Byrd, D. Personal Communication. 1998.

Google Scholar

[3]

Croft, W. B. and D. J. Harper. "Using probabilistic models of document retrieval without relevance information." Journal of Documentation, 35, 1979 (pp. 285-295).

Crossref

Google Scholar

[4]

Fuhr, N. "Models for Retrieval with Probabilistic Indexing" Information Processing and Management, v. 25, no. 1 1989.

Digital Library

Google Scholar

[5]

Ghosh, M. J., T. Hwang and K. W. Tsui. "Construction of Improved Estimators in Multiparameter Estimation for Discrete Exponential Families." Annals of Statistics, v. 11, 351-367.

Crossref

Google Scholar

[6]

Greenwood, M. and G. U. Yule. "An Inquiry into the Nature of Frequency Distribution Representative of Multiple Happenings with Particular Reference to the Occurrence of Multiple Attacks of Disease or of Repeated Accidents." Journal of the Royal Statistical Society, v. 83, pp. 255-279, 1920.

Crossref

Google Scholar

[7]

Harter, S. P. "A Probabilistic Approach to Automatic Keyword Indexing" Journal of the American Society for Information Science, July-August, 1975.

Google Scholar

[8]

Kalt, T. "A New Probabilistic Model of Text Classification and Retrieval", CIIR Tech. Report No. 78, 1996.

Google Scholar

[9]

Kwok, K. L. "A New Method of Weighting Query Terms for Ad-Hoc Retrieval", In proceedings of ACM SIGIR 1996, pp 187-195.

Digital Library

Google Scholar

[10]

Margulis, E. L. "Modeling documents with multiple Poisson distributions." Information Processing and Management v. 29 no. 2 pp. 215-227, 1993.

Digital Library

Google Scholar

[11]

Parzen, E. "On estimation of a probability density function and mode." Annals of Mathematical Statistics, vol. 33, 1962.

Crossref

Google Scholar

[12]

Robertson, S. E. and S. Walker. Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. In proceedings of ACM SIGIR 1994. pp. 232-241.

Digital Library

Google Scholar

[13]

Ponte, J. M. and W. B. Croft. "Text Segmentation by Topic," in Proceedings of the First European Conference on Research and Advanced Technology for Digital Libraries, 1997.

Digital Library

Google Scholar

[14]

Robertson, S. E. and K. Sparck Jones. "Relevance Weighting Of Search Terms," Journal of the American Society for Information Science, vol. 27, 1977.

Google Scholar

[15]

Salton, G. Automatic Text Processing. Addison Wesley, 1989.

Digital Library

Google Scholar

[16]

Silverman, B. W. Density Estimation for Statistics and Data Analysis Chapman and Hall, 1986.

Google Scholar

[17]

Terrell, G. R. and D. W. Scott. "Oversmoothed Nonparametric Density Estimators" Journal of the American Statistical Association. Vol. 3, Number 389, 1985.

Google Scholar

[18]

Titterington, D. M., U. E. Makov and A. F. M Smith. Statistical Analysis of Finite Mixture Distributions John Wiley and Sons, 1985.

Google Scholar

[19]

Turtle H. and W. B. Croft. "Efficient Probabilistic Inference for Text Retrieval," Proceedings of RIAO 3, 1991.

Google Scholar

[20]

Wong, S. K. M. and Y. Y. Yao. "A Probability Distribution Model for Information Retrieval" Information Processing and Management, v. 25 no. 1 pp. 39-53, 1989.

Digital Library

Google Scholar

[21]

Yamron, J. "Topic Detection and Tracking Segmentation Task" In proceedings of The Topic Detection and Tracking Workshop, Oct. 1997.

Google Scholar

[22]

Zelen, M. and N. Severo. "Probability Functions" Handbook of Mathematical Functions. M. Abramowitz and I. A. Stegun ed. National Bureau of Standards Applied Mathematics Series No. 55, 1964.

Google Scholar

Cited By

View all

Yang HLi SGonçalves T(2024)Enhancing Biomedical Question Answering with Large Language ModelsInformation10.3390/info1508049415:8(494)Online publication date: 19-Aug-2024
https://doi.org/10.3390/info15080494
Rajaei DTaheri ZFani HSerra ESpezzano F(2024)No Query Left Behind: Query Refinement via BacktranslationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679729(1961-1972)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679729
Liu BZhu ZAi QLiu YWu YSerra ESpezzano F(2024)LeDQA: A Chinese Legal Case Document-based Question Answering DatasetProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679154(5385-5389)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679154
Show More Cited By

Index Terms

A Language Modeling Approach to Information Retrieval

Index terms have been assigned to the content through auto-classification.

Recommendations

A language modeling approach to information retrieval
Structured queries, language modeling, and relevance modeling in cross-language information retrieval
Special issue: Cross-language information retrieval

Two probabilistic approaches to cross-lingual retrieval are in wide use today, those based on probabilistic models of relevance, as exemplified by INQUERY, and those based on language modeling. INQUERY, as a query net model, allows the easy incorporation ...
A multi-dependency language modeling approach to information retrieval
PAKDD'07: Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining

This paper presents a multi-dependency language modeling approach to information retrieval. The approach extends the basic KL-divergence retrieval approach by introducing the hybrid dependency structure, which includes syntactic dependency, syntactic ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ACM SIGIR Forum Volume 51, Issue 2

SIGIR Test-of-Time Awardees 1978-2001

July 2017

276 pages

ISSN:0163-5840

DOI:10.1145/3130348

Editors:
Donna Harman
National Institutes of Science & Technology, Gaithersburg MD, USA
,
Diane Kelly
University of Tennessee, Knoxville TN, USA

Issue’s Table of Contents

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 August 2017

Published in SIGIR Volume 51, Issue 2

Check for updates

Qualifiers

Column

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

47
Total Citations
View Citations
958
Total Downloads

Downloads (Last 12 months)307
Downloads (Last 6 weeks)23

Reflects downloads up to 31 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Yang HLi SGonçalves T(2024)Enhancing Biomedical Question Answering with Large Language ModelsInformation10.3390/info1508049415:8(494)Online publication date: 19-Aug-2024
https://doi.org/10.3390/info15080494
Rajaei DTaheri ZFani HSerra ESpezzano F(2024)No Query Left Behind: Query Refinement via BacktranslationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679729(1961-1972)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679729
Liu BZhu ZAi QLiu YWu YSerra ESpezzano F(2024)LeDQA: A Chinese Legal Case Document-based Question Answering DatasetProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679154(5385-5389)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679154
Zhuang SZhuang HKoopman BZuccon GHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language ModelsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657813(38-47)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657813
Wang ZXu ZSrikumar VAi QChua TNgo CKa-Wei Lee RKumar RLauw H(2024)An In-depth Investigation of User Response Simulation for Conversational SearchProceedings of the ACM Web Conference 202410.1145/3589334.3645447(1407-1418)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645447
Zhu QZhang HHe QDou Z(2024)Query-Aware Explainable Product Search With Reinforcement Knowledge Graph ReasoningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.329733136:3(1260-1273)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3297331
Quevedo ECerny TRodriguez ARivas PYero JSooksatra KZhakubayev ATaibi D(2024)Legal Natural Language Processing From 2015 to 2022: A Comprehensive Systematic Mapping Study of Advances and ApplicationsIEEE Access10.1109/ACCESS.2023.333394612(145286-145317)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3333946
Yan SZhang Z(2024)SKYPERInformation Sciences: an International Journal10.1016/j.ins.2024.121162682:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.121162
Annepaka YPakray P(2024)Large language models: a survey of their development, capabilities, and applicationsKnowledge and Information Systems10.1007/s10115-024-02310-4Online publication date: 31-Dec-2024
https://doi.org/10.1007/s10115-024-02310-4
Chatterjee SSarkar KPatra S(2024)A Hybrid Query Expansion Method for Effective Bengali Information RetrievalProceedings of 4th International Conference on Frontiers in Computing and Systems10.1007/978-981-97-2611-0_26(377-397)Online publication date: 29-Jun-2024
https://doi.org/10.1007/978-981-97-2611-0_26
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

A language modeling approach to information retrieval

Structured queries, language modeling, and relevance modeling in cross-language information retrieval

A multi-dependency language modeling approach to information retrieval