The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Cross-language information retrieval based on parallel texts and automatic mining of parallel texts from the Web
Abstract
This paper describes the use of a probabilistic translation model to cross-language IR (CLIR). The performance of this approach is compared with that using machine translation (MT). It is shown that using a probabilistic model, we are able to obtain performances close to those using an MT system. In addition, we also investigated the possibility of automatically gather parallel texts from the Web in an attempt to construct a reasonable training corpus. The result is very encouraging. We showed that in several tests, such a training corpus is as good as a manually constructed one for CLIR purposes.
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
Cites
Introduction to Modern Information Retrieval
Salton, G., & McGill, M. J. (1983) · Virtual Defense Library (Ministerio de Defensa) · Cited by 9,680
The mathematics of statistical machine translation: parameter estimation
Brown, P. F., Pietra, V. J. D., Pietra, S. A. D., & Mercer, R. L. (1993) · Cited by 3,973
A program for aligning sentences in bilingual corpora
Gale, W. A., & Church, K. (1993) · doi:10.5555/972450.972455 · Cited by 945
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
Implementation of the SMART Information Retrieval System
Buckley, C. (1985) · eCommons (Cornell University) · Cited by 289
Cross-Language Information Retrieval
Grefenstette, G. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5661-9 · Cited by 227
Translingual information retrieval: learning from bilingual corpora
Yang, Y., Carbonell, J., Brown, R. D., & Frederking, R. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(98)00063-0 · Cited by 101
Parallel Strands: A Preliminary Investigation into Mining the Web for Bilingual Text
Resnik, P. (1998) · Lecture notes in computer science · doi:10.1007/3-540-49478-2_7 · Cited by 99
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
Transfer learning for cross-language text categorization through active correspondences construction
Zhou, J., Pan, S., Tsang, I., & Ho, S.-S. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10211 · Cited by 50
Metadata
- Authors
- Nie, J.-Y., Simard, M., Isabelle, P., & Durand, R.
- Venue
- Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 1999-08
- Pages
- 74-81
- OpenAlex
- W2024181699
- Cited by
- 306
- References
- 8
- FWCI
- 20.78
- 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: Cross-language information retrieval based on parallel texts and automatic mining of parallel texts from the Web. https://app.cochat.ai/papers/10.1145/312624.312656
- The paper
- Nie, J.-Y., Simard, M., Isabelle, P., & Durand, R. (1999). Cross-language information retrieval based on parallel texts and automatic mining of parallel texts from the Web. Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/312624.312656
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.
