article

Free access

Local Feedback in Full-Text Retrieval Systems

Authors:

R. Attar,

A. S. FraenkelAuthors Info & Claims

Journal of the ACM (JACM), Volume 24, Issue 3

Pages 397 - 417

https://doi.org/10.1145/322017.322021

Published: 01 July 1977 Publication History

PDF eReader

References

[1]

ARTANDI, S, AND WOLF, E H The effectiveness of automatically generated weights and hnks in mechamcal indexing Amer Document 20 (1969), 198-202

Google Scholar

[2]

ATTAR, R, CHOOEKA, xlr, DERSHOWITZ, N, AND FRAENKEL, A S KEDMA-hngmstic tools for retrieval systems Tech Rep No 4, Inst for Information Retrieval and Computational Linguistics (IRCOL), Bar-Ilan U, Ramat Gan, Israel, June 1976

Google Scholar

[3]

ATTAR, R, FRAENKEL, A S, AND STEIN, J Local feedback in full-text English and Hebrew retrieval systems Tech Rep No 5, Inst for Information Retrieval and Computational Linguistics (IRCOL), Bar-Ilan U, Ramat Gan, Israel, Feb 1976

Google Scholar

[4]

AUGUSTSON, J G, AND MINKER, J An analys~s of some graph theoretical cluster techmques J ACM 17, 4 (Oct 1970), 571-588

Crossref

Google Scholar

[5]

CHOUEKA, Y, COHEN, M, OUECK, J, FRAENKEL, A S, AND SLAE, M Full text document retrieval Hebrew legal texts (Report on the first phase of the Responsa Retrieval Project). Proc ACM Symp on Information Storage and Retrieval, J Mlnker and S Rosenteld, Eds, U of Maryland, College Park, Md, 1971, pp 61-79 An expanded version of this paper appeared as Heft 3, Arbeztspapzere Rechtsmformattk, J Schweltzer Verlag, 1972

Crossref

Google Scholar

[6]

CHOUEKA, Y, AND SHAPIRO, M Machine analysis of Hebrew morphology potentialities and achievements (in Hebrew) Leshonenu 27 (1964), 354-372 English abstract, The Fm:te String 3, 5 (1964), 6

Google Scholar

[7]

DOYLE, L B Semantic road maps for hterature searches J ACM 8, 4 (Oct 1961), 553-578

Crossref

Google Scholar

[8]

EDMUNOSON, H P, ANn WYLLYS, R E Automatic abstracting and indexing-survey and recommendations Comm ACM 4, 5 (May 1961), 226-234

Crossref

Google Scholar

[9]

FRAENKEL, A S Legal Information retrieval In Advances tn Computers, Vol 9, F L Alt and M Rubinoff, Eds, Academic Press, New York, 1968, pp 113-178

Google Scholar

[10]

FRAENKEL, A S All about the Responsa Retrieval Project you always wanted to know but were afraid to ask Expanded summary, Proc Third Symp Legal Data Processing in Europe, Oslo, July 1975, pp 131-141, also Jurtmetr~cs J 16 (1976), 149-156, also Informa:zca e D~rltto (to appear)

Google Scholar

[11]

LESK, M E Word-word associations in document retrieval systems Amer Document 20 (1969), 27-38

Google Scholar

[12]

M~NKER, J, PELTOLA, E, AND WILSON, G A Document retrieval experiments using cluster analysis d Amer Soc Inform Sct 24 (1973), 246-260

Google Scholar

[13]

MINKER, J, WILSON, G A, AND ZIMMERMAN. B H An evaluation of query expansion by the addition of clustered terms for a document retrmval system J Inform Stor Retr 8 (1972), 329-348

Google Scholar

[14]

SALTON, G Automatw Information Orgamzatton and Retrieval McGraw-Hall, New York, 1968

Crossref

Google Scholar

[15]

SALTON, G, Ed The SMART Retrieval System, Experiments tn Automatic Document Processing Prentice-Hall, Englewood Chffs, N J . 1971

Crossref

Google Scholar

[16]

SPARCK-JONES, K Automatic Keyword Classification for Information Retrieval Butterworth, London, 1971

Google Scholar

[17]

SPARCK-JONES, K, AND BARBER, E O What makes an automatic keyword classification effectwe'~ J Amer Soc Inform Scl 22 (1971), 166-175

Google Scholar

[18]

STILES, H E The association factor m reformation retrieval J ACM 8, 2 (April 1961), 271-279

Crossref

Google Scholar

[19]

Yu, C T A methodology for the construction of term classes J Inform Stor Retr 10 (1974), 2,13- 251

Google Scholar

Cited By

View all

Zhu YPang LWu KLan YShen HCheng X(2024)Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language UnderstandingACM Transactions on Information Systems10.1145/365259942:5(1-29)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3652599
Zhu HHuang JRudinac SKanoulas EGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language ModelsProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658032(978-987)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658032
Chi WTang TSalleh NMukred MAlSalman HZohaib M(2024)Data Augmentation With Semantic Enrichment for Deep Learning Invoice Text ClassificationIEEE Access10.1109/ACCESS.2024.338786012(57326-57344)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3387860
Show More Cited By

Index Terms

Local Feedback in Full-Text Retrieval Systems
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

Recommendations

Incremental Relevance Feedback in Japanese Text Retrieval
Abstract
The application of relevance feedback techniques has been shown to improve retrieval performance for a number of information retrieval tasks. This paper explores incremental relevance feedback for ad hoc Japanese text retrieval; examining, ...
Conceptual information retrieval from full-text
RIAO '88: User-Oriented Content-Based Text and Image Handling

In this paper we report on our attempts to perform full-text information retrieval using a technique that is similar in spirit to frame-based methods in natural language understanding. In our prototype system, called RUBRIC, we employ concepts and their ...
Does pseudo-relevance feedback improve distributed information retrieval systems?

This paper presents a thorough analysis of the capabilities of the pseudo-relevance feedback (PRF) technique applied to distributed information retrieval (DIR). Previous studies have researched the application of PRF to improve the selection process of ...

Comments

Information & Contributors

Information

Published In

Journal of the ACM Volume 24, Issue 3

July 1977

175 pages

ISSN:0004-5411

EISSN:1557-735X

DOI:10.1145/322017

Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 1977

Published in JACM Volume 24, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

195
Total Citations
View Citations
1,306
Total Downloads

Downloads (Last 12 months)70
Downloads (Last 6 weeks)7

Reflects downloads up to 11 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zhu YPang LWu KLan YShen HCheng X(2024)Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language UnderstandingACM Transactions on Information Systems10.1145/365259942:5(1-29)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3652599
Zhu HHuang JRudinac SKanoulas EGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language ModelsProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658032(978-987)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658032
Chi WTang TSalleh NMukred MAlSalman HZohaib M(2024)Data Augmentation With Semantic Enrichment for Deep Learning Invoice Text ClassificationIEEE Access10.1109/ACCESS.2024.338786012(57326-57344)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3387860
Carroll ABorycz J(2024)Integrating large language models and generative artificial intelligence tools into information literacy instructionThe Journal of Academic Librarianship10.1016/j.acalib.2024.10289950:4(102899)Online publication date: Jul-2024
https://doi.org/10.1016/j.acalib.2024.102899
Gupta VDixit A(2023)Recent Query Reformulation Approaches for Information Retrieval System - A SurveyRecent Advances in Computer Science and Communications10.2174/266625581566622040409192016:1Online publication date: Jan-2023
https://doi.org/10.2174/2666255815666220404091920
Peng WChunhui H(2023)Research on Optimization Methods for Industrial Model Retrieval2023 5th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP58431.2023.00083(425-433)Online publication date: Mar-2023
https://doi.org/10.1109/ICNLP58431.2023.00083
bahrani pMinaei Bidgoli BParvin HMirzarezaee MKeshavarz A(2022)An Ontological Hybrid Recommender System for Dealing with Cold Start ProblemSignal and Data Processing10.52547/jsdp.19.1.119:1(1-18)Online publication date: 1-May-2022
https://doi.org/10.52547/jsdp.19.1.1
Zhu YPang LLan YShen HCheng XAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)LoLProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532017(825-836)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532017
Zamani HDiaz FDehghani MMetzler DBendersky MAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Retrieval-Enhanced Machine LearningProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531722(2875-2886)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531722
Shukla ADas SKumar P(2021)WordNet Based Hybrid Model for Query Expansion2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES)10.1109/TRIBES52498.2021.9751671(1-6)Online publication date: 17-Dec-2021
https://doi.org/10.1109/TRIBES52498.2021.9751671
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

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

References

Cited By

Index Terms

Recommendations

Incremental Relevance Feedback in Japanese Text Retrieval

Conceptual information retrieval from full-text

Does pseudo-relevance feedback improve distributed information retrieval systems?

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Get Access

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations