The DOI matches its Crossref record.
Local Feedback in Full-Text Retrieval Systems
Open accessAbstract
AaSTRACT.In a full-text natural-language retrieval system, local feedbacl~ is the process of formulating a new ~mproved search based on clustering terms from the documents returned m a previous search of any given query Experiments were run on a database of US patents It ~s concluded that m contrast toglobalclustermg, where the size of matrices hmmts apphcatmns to small databases and improvements are doubtful, local clustering is practical also for large databases and appears to improve overall performance, especially tf metrical constraints and weighting by proximity are embedded m the local feedback The local methods adapt themselves to each mdwtdual search and produce useful searchonymsterms which are "synonymous" m the context of one query Searchonyms lead to new ~mproved search formulahons both via manual and vm automahc feedback
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Quary Expansion Using Local and Global Document Analysis
Xu, J., & Croft, W. B. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130364 · Cited by 1,252
A Survey of Automatic Query Expansion in Information Retrieval
Carpineto, C., & Romano, G. (2012) · ACM Computing Surveys · doi:10.1145/2071389.2071390 · Cited by 1,109
Probabilistic query expansion using query logs
Cui, H., Wen, J.-R., Nie, J.-Y., & Ma, W.-Y. (2002) · Proceedings of the 11th international conference on World Wide Web · doi:10.1145/511446.511489 · Cited by 423
Query expansion techniques for information retrieval: a survey
Azad, H. K., & Deepak, A. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.05.009 · Cited by 313
Resolving ambiguity for cross-language retrieval
Ballesteros, L., & Croft, W. B. (1998) · Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/290941.290958 · Cited by 309
Metadata
- Authors
- Attar, R., & Fraenkel, A. S.
- Venue
- Journal of the ACM
- Published
- 1977-07
- Volume
- 24
- Issue
- 3
- Pages
- 397-417
- ISSN
- 0004-5411 (ISSN-L)
- 1557-735X
- OpenAlex
- W1992113527
- Cited by
- 230
- References
- 0
- FWCI
- 16.38
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Local Feedback in Full-Text Retrieval Systems. https://app.cochat.ai/papers/10.1145/322017.322021
- The paper
- Attar, R., & Fraenkel, A. S. (1977). Local Feedback in Full-Text Retrieval Systems. Journal of the ACM. https://doi.org/10.1145/322017.322021
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
