The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Unsupervised word sense disambiguation rivaling supervised methods
Open accessAbstract
This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations. The algorithm is based on two powerful constraints---that words tend to have one sense per discourse and one sense per collocation---exploited in an iterative bootstrapping procedure. Tested accuracy exceeds 96%.
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
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Maximum Likelihood from Incomplete Data via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1976) · Cited by 4,517
Introduction to WordNet: An On-line Lexical Database*
Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. J. (1990) · International Journal of Lexicography · doi:10.1093/ijl/3.4.235 · Cited by 4,197
Lesk, M. (1986) · Proceedings of the 5th annual international conference on Systems documentation - SIGDOC '86 · doi:10.1145/318723.318728 · Cited by 1,770
Baum, L. E. (1972) · Medical Entomology and Zoology · Cited by 1,405
Inference and disputed authorship : The Federalist
Mosteller, F. C., & Wallace, D. L. (1964) · Medical Entomology and Zoology · Cited by 904
Rivest, R. L. (1987) · Machine Learning · doi:10.1007/bf00058680 · Cited by 886
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
Yarowsky, D. (1992) · Proceedings of the 14th conference on Computational linguistics - · doi:10.3115/992133.992140 · Cited by 663
Self-citationA method for disambiguating word senses in a large corpus
Gale, W. A., Church, K., & Yarowsky, D. (1992) · Computers and the Humanities · doi:10.1007/bf00136984 · Cited by 532
Self-citationYarowsky, D. (1993) · Proceedings of the workshop on Human Language Technology - HLT '93 · doi:10.3115/1075671.1075731 · Cited by 376
Self-citation
Cited by
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791
A survey on semi-supervised learning
van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666
Self-Training With Noisy Student Improves ImageNet Classification
Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01070 · Cited by 2,393
Toward an architecture for never-ending language learning
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E., & Mitchell, T. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7519 · Cited by 2,000
Introduction to Semi-Supervised Learning
Zhu, X., & Goldberg, A. B. (2009) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.1007/978-3-031-01548-9 · Cited by 1,766
Metadata
- Authors
- Yarowsky, D.
- Venue
- Proceedings of the 33rd annual meeting on Association for Computational Linguistics -
- Published
- 1995
- Pages
- 189-196
- OpenAlex
- W2101210369
- Cited by
- 2,385
- References
- 18
- FWCI
- 25.24
- Type
- conference paper
- 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: Unsupervised word sense disambiguation rivaling supervised methods. https://app.cochat.ai/papers/10.3115/981658.981684
- The paper
- Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of the 33rd annual meeting on Association for Computational Linguistics -. https://doi.org/10.3115/981658.981684
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.
