The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Randomized algorithms and NLP: using locality sensitive hash function for high speed noun clustering
Abstract
In this paper, we explore the power of randomized algorithm to address the challenge of working with very large amounts of data. We apply these algorithms to generate noun similarity lists from 70 million pages. We reduce the running time from quadratic to practically linear in the number of elements to be computed.
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
Approximate nearest neighbors: towards removing the curse of dimensionality
Indyk, P., & Motwani, R. (1998) · Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98 · doi:10.1145/276698.276876 · Cited by 4,188
Goemans, M. X., & Williamson, D. P. (1995) · Journal of the ACM · doi:10.1145/227683.227684 · Cited by 3,587
Cited by
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Andoni, A., & Indyk, P. (2008) · Communications of the ACM · doi:10.1145/1327452.1327494 · Cited by 1,437
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
Andoni, A., & Indyk, P. (2006) · 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06) · doi:10.1109/focs.2006.49 · Cited by 1,067
Semantic taxonomy induction from heterogenous evidence
Snow, R., Jurafsky, D., & Ng, A. Y. (2006) · Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06 · doi:10.3115/1220175.1220276 · Cited by 441
Vector Space Models of Word Meaning and Phrase Meaning: A Survey
Erk, K. (2012) · Language and Linguistics Compass · doi:10.1002/lnco.362 · Cited by 321
Kilgarriff, A. (2007) · Computational Linguistics · doi:10.1162/coli.2007.33.1.147 · Cited by 286
Metadata
- Authors
- Ravichandran, D., Pantel, P., & Hovy, E.
- Venue
- Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05
- Published
- 2005
- Pages
- 622-629
- OpenAlex
- W2122056984
- Cited by
- 208
- References
- 2
- FWCI
- 7.39
- 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: Randomized algorithms and NLP: using locality sensitive hash function for high speed noun clustering. https://app.cochat.ai/papers/10.3115/1219840.1219917
- The paper
- Ravichandran, D., Pantel, P., & Hovy, E. (2005). Randomized algorithms and NLP: using locality sensitive hash function for high speed noun clustering. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05. https://doi.org/10.3115/1219840.1219917
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.