The DOI matches its Crossref record.
Experiments in multilingual information retrieval using the SPIDER system
Abstract
We introduce a new approach to multilingual information retrieval based on the use of thesaurus-based query expansion techniques applied over a collection of comparable multilingual documents. This approach has been built into the SPIDER information retrieval system and has been tested over a large collection of Italian documents. We have shown that the SPIDER system retrieves Italian documents in response to user queries written in German with better effectiveness than a baseline system evaluating Italian queries against Italian documents. Although the importantance of the SPIDER stemming algorithm for Italian must be stressed in these results, we have also achieved performance on multilingual retrieval tasks within 32% of the best SPIDER performance on Italian retrieval, by including a relevance feedback loop in the task of multilingual retrieval. 1 Introduction As far back as the early 1970's Prof. Gerard Salton at Cornell University performed experiments to investigate the feasib...
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
Translation techniques in cross-language information retrieval
Zhou, D., Truran, M., Brailsford, T., Wade, V., & Ashman, H. (2012) · ACM Computing Surveys · doi:10.1145/2379776.2379777 · Cited by 698
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
- Sheridan, P., & Ballerini, J. P.
- Venue
- Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '96
- Publisher
- ACM Press
- Published
- 1996
- Pages
- 58-65
- OpenAlex
- W2047959359
- Cited by
- 207
- References
- 0
- FWCI
- 14.26
- Type
- conference paper
- Language
- en
- Open access
- closed
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: Experiments in multilingual information retrieval using the SPIDER system. https://app.cochat.ai/papers/10.1145/243199.243213
- The paper
- Sheridan, P., & Ballerini, J. P. (1996). Experiments in multilingual information retrieval using the SPIDER system. Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '96. https://doi.org/10.1145/243199.243213
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.