Article

Free access

Document and passage retrieval based on hidden Markov models

Authors:

SIGIR '94: Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 318 - 327

Published: 01 August 1994 Publication History

PDF eReader

References

[1]

J. S. Bridle. Stochastic Models and Template Matching: some Important Relationships Between Two Apparently Different Techniques for Automatic Speech Recognition. In Institute of Acoustics, Autumn Conference, pages 452a-452h, 1984.

Google Scholar

[2]

N. Fuhr. Probabilistic Models in Information Retrieval. The Computer Journal, 35(3):243-255, 1992.

Digital Library

Google Scholar

[3]

U. Hahn. Topic Parsing: Accounting for Macro Structures in Full-Text Analysis. Information Processing gJ Management, 26(1):135-170, 1990.

Digital Library

Google Scholar

[4]

Y. He, N.Y. Chen, and A. Kundu. Handwritten Word Recognition Using HMM with Adaptive Length Viterbi Algorithm. In International Conference on Acoustics, Speech, and Signal Processing, pages III-153-III-156, 1992.

Google Scholar

[5]

M. A. Hearst and C. Plaunt. Subtopic Structuring for Full-Length Access. In A CM SIGIR Conference on R~D in Information Retrieval, pages 59-68, 1993.

Digital Library

Google Scholar

[6]

X. D. Huang, Y. Ariki, and M. A. Jack. Hidden Markov Models for Speech Recognition. Edinburgh University Press, Edinburgh, 1990.

Digital Library

Google Scholar

[7]

J. J. Hull. A Hidden Markov Model for Language Syntax in Text Recognition. In 11th IAPR International Conference on Pattern Recognition. Vol.II, pages 124-127, 1992.

Crossref

Google Scholar

[8]

R. Kuhlen and M. Hess. Passagen-Retrieval-auch eine MSglichkeit der automatischen Verknfipfung in Hypertexten. In Information Retrieval'93, pages 100-115, Konstanz, Deutschland, October, 1993. Hochschulverband fiir Informationswissenschaft (HI).

Google Scholar

[9]

J. Kupiec. Hidden Markov Estimation for Unrestricted Stochastic Context-Free Grammars. In international Conference on Acoustics, Speech, and Signal Processing, pages 1-177-180, 1992.

Google Scholar

[10]

J. Kupiec. Robust Part-of-Speech Tagging Using a Hidden Markov Model. Computer Speech and Language, 6:225-242, 1992.

Crossref

Google Scholar

[11]

L. W. Levinson, L. R. Rabiner, and M. M. Sondhi. An Introduction to the Application of the Theory of Probabilistic Functions of Markov Process to Automatic Speech Recognition. The Bell System Technical Journal, 62(4):1035-1075, 1983.

Crossref

Google Scholar

[12]

Y. Linde, A. Buzo, and R. M. Gray. An Algorithm for Vector Quantizer Design. IEEE Transactions on Communication, 28(1):84-95, 1980.

Crossref

Google Scholar

[13]

A. Moffat, R. Sacks-Davis, R. Wilkinson, and J. Zobel. Retrieval of Partial Documents. In TREC-2 Proceedings, 1993.

Google Scholar

[14]

J. O'Connor. Retrieval of Answer-Sentences and Answer-Figures from Papers by Text Searching. Information Processing ~ Management, 11(5/7):155-164, 1975.

Google Scholar

[15]

J. O'Connor. Answer Passage Retrieval by Text Searching. Journal of the ASIS, 31(4):227-239, 1980.

Google Scholar

[16]

M. F. Porter. An Algorithm for Suffix Stripping. Program, 14(3):130-137, 1980.

Crossref

Google Scholar

[17]

S. E. Robertson. The Probability Ranking Principle in IR. Journal of Documentation, 33(4):294-304, 1977.

Crossref

Google Scholar

[18]

R. C. Rose and D. B. Paul. A Hidden Markov Model Based Keyword Recognition System. in International Conference on Acoustics, Speech, and Signal Processing, pages 129-132, 1990.

Crossref

Google Scholar

[19]

G. Salton, J. Allan, and C. Buckley. Approaches to Passage Retrieval in Full Text Information Systems. In A CM SIGIR Conference on Reid in Information Retrieval, pages 49-58, 1993.

Digital Library

Google Scholar

[20]

G. Salton and C. Buckley. Term Weighting Approaches in Automatic Text Retrieval. Information Processing ~ Management, 24(5):513-523, 1988.

Digital Library

Google Scholar

[21]

P. Schiiuble and U. Glavitsch. A Probabilistic Hypermedia Retrieval Model Based on Hidden Markov Models. Workshop Intelligent Access to Information Systems, Darmstadt, Nov. 1990.

Google Scholar

[22]

C. Stanfill and D. Waltz. Statistical Methods, Artificial Intelligence, and Information Retrieval. In P. S. Jacobs, editor, Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval, pages 215-226. Lawrence Erlbaum Associates, 1992.

Digital Library

Google Scholar

[23]

C. J. van Rijsbergen. Information Retrieval. Butterworths, London, second edition, 1979.

Digital Library

Google Scholar

[24]

L. D. Wilcox and M. A. Bush. HMM-Based Wordspotting for Voice Editing and Indexing. in European Conference on Speech Communication and Technology (EUROSPEECH), pages 25-28, 1991.

Google Scholar

[25]

P. Willet and A. M. Robertson. Searching for Historical Word-Forms in a Database of 17th-Century English Text Using Spelling-Correction Methods. In N. Belkin, P. Ingwersen, and A. M. Pejtersen, editors, A CM SIGIR Conference on R~D in Information Retrieval, pages 256-265, 1992.

Digital Library

Google Scholar

Cited By

View all

Su ZDou ZZhu YWen J(2024)Passage-aware Search Result DiversificationACM Transactions on Information Systems10.1145/365367242:5(1-29)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3653672
Costa ABuccio EMelucci M(2015)A Document Retrieval Model Based on Digital Signal FilteringACM Transactions on Information Systems10.1145/280978734:1(1-37)Online publication date: 22-Sep-2015
https://dl.acm.org/doi/10.1145/2809787
Na S(2015)Two-Stage Document Length Normalization for Information RetrievalACM Transactions on Information Systems10.1145/269966933:2(1-40)Online publication date: 17-Feb-2015
https://dl.acm.org/doi/10.1145/2699669
Show More Cited By

Index Terms

Document and passage retrieval based on hidden Markov models
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Retrieval models and ranking
2. Theory of computation
  1. Models of computation
    1. Probabilistic computation
  2. Semantics and reasoning
    1. Program reasoning
      1. Abstraction

Recommendations

Coding with partially hidden Markov models
DCC '95: Proceedings of the Conference on Data Compression

Partially hidden Markov models (PHMM) are introduced. They are a variation of the hidden Markov models (HMM) combining the power of explicit conditioning on past observations and the power of using hidden states. (P)HMM may be combined with arithmetic ...
Mixture-State Document Segmentation Using Wavelet-Domain Hidden Markov Tree Models
WAA '01: Proceedings of the Second International Conference on Wavelet Analysis and Its Applications

In this paper we introduce a mixture-state document segmentation method based on wavelet and the hidden Markov tree (HMT) models. First we propose a three-state HMT segmentation method that is similar to those in the reference [1]. Then through ...
Structural hidden Markov models: An application to handwritten numeral recognition

We introduce in this paper a generalization of the widely used hidden Markov models (HMM's), which we name "structural hidden Markov models" (SHMM). Our approach is motivated by the need of modeling complex structures which are encountered in many ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SIGIR '94: Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval

August 1994

363 pages

ISBN:038719889X

Editors:
W. Bruce Croft
Univ. of Massachusetts, Amherst
,
C. J. van Rijsbergen
Univ. of Glasgow, Glasgow, Scotland

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 August 1994

Check for updates

Qualifiers

Article

Conference

SIGIR94

Sponsor:

AICA
Irish Comp Soc
SIGIR
BCS-IRSG
BCS-IRSB
Dublin City University

SIGIR94: 17th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval

July 3 - 6, 1994

Dublin, Ireland

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

23
Total Citations
View Citations
673
Total Downloads

Downloads (Last 12 months)22
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Su ZDou ZZhu YWen J(2024)Passage-aware Search Result DiversificationACM Transactions on Information Systems10.1145/365367242:5(1-29)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3653672
Costa ABuccio EMelucci M(2015)A Document Retrieval Model Based on Digital Signal FilteringACM Transactions on Information Systems10.1145/280978734:1(1-37)Online publication date: 22-Sep-2015
https://dl.acm.org/doi/10.1145/2809787
Na S(2015)Two-Stage Document Length Normalization for Information RetrievalACM Transactions on Information Systems10.1145/269966933:2(1-40)Online publication date: 17-Feb-2015
https://dl.acm.org/doi/10.1145/2699669
Bost XSenay GEl-Bèze MDe Mori R(2015)Multiple topic identification in human/human conversationsComputer Speech and Language10.1016/j.csl.2015.03.00634:1(18-42)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1016/j.csl.2015.03.006
Galuščáková PPecina PKankanhalli MRueger SManmatha RJose Jvan Rijsbergen K(2014)Experiments with Segmentation Strategies for Passage Retrieval in Audio-Visual DocumentsProceedings of International Conference on Multimedia Retrieval10.1145/2578726.2578753(217-224)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1145/2578726.2578753
Krikon EKurland OBendersky M(2010)Utilizing inter-passage and inter-document similarities for reranking search resultsACM Transactions on Information Systems10.1145/1877766.187776929:1(1-28)Online publication date: 27-Dec-2010
https://dl.acm.org/doi/10.1145/1877766.1877769
Bendersky MKurland O(2008)Utilizing passage-based language models for document retrievalProceedings of the IR research, 30th European conference on Advances in information retrieval10.5555/1793274.1793297(162-174)Online publication date: 30-Mar-2008
https://dl.acm.org/doi/10.5555/1793274.1793297
Wang MSi LChua TLeong MMyaeng SOard DSebastiani F(2008)Discriminative probabilistic models for passage based retrievalProceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval10.1145/1390334.1390407(419-426)Online publication date: 20-Jul-2008
https://dl.acm.org/doi/10.1145/1390334.1390407
Oh HMyaeng SJang M(2007)Semantic passage segmentation based on sentence topics for question answeringInformation Sciences: an International Journal10.1016/j.ins.2007.02.038177:18(3696-3717)Online publication date: 1-Sep-2007
https://dl.acm.org/doi/10.1016/j.ins.2007.02.038
Jiang JZhai CHerzog OSchek HFuhr NChowdhury ATeiken W(2005)Accurately extracting coherent relevant passages using hidden Markov modelsProceedings of the 14th ACM international conference on Information and knowledge management10.1145/1099554.1099631(289-290)Online publication date: 31-Oct-2005
https://dl.acm.org/doi/10.1145/1099554.1099631
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

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

Coding with partially hidden Markov models

Mixture-State Document Segmentation Using Wavelet-Domain Hidden Markov Tree Models

Structural hidden Markov models: An application to handwritten numeral recognition