The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A web-based kernel function for measuring the similarity of short text snippets
Abstract
Determining the similarity of short text snippets, such as search queries, works poorly with traditional document similarity measures (e.g., cosine), since there are often few, if any, terms in common between two short text snippets. We address this problem by introducing a novel method for measuring the similarity between short text snippets (even those without any overlapping terms) by leveraging web search results to provide greater context for the short texts. In this paper, we define such a similarity kernel function, mathematically analyze some of its properties, and provide examples of its efficacy. We also show the use of this kernel function in a large-scale system for suggesting related queries to search engine users.
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
A vector space model for automatic indexing
Salton, G., Wong, A., & Yang, C. S. (1975) · Communications of the ACM · doi:10.1145/361219.361220 · Cited by 7,521
Cited by
Empirical study of topic modeling in Twitter
Hong, L., & Davison, B. D. (2010) · Proceedings of the First Workshop on Social Media Analytics · doi:10.1145/1964858.1964870 · Cited by 1,120
TAGME: on-the-fly annotation of short text fragments (by wikipedia entities)
Ferragina, P., & Scaiella, U. (2010) · Proceedings of the 19th ACM international conference on Information and knowledge management · doi:10.1145/1871437.1871689 · Cited by 743
Scaling up all pairs similarity search
Bayardo, R. J., Ma, Y., & Srikant, R. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242591 · Cited by 673
Chen (2010) · doi:10.1145/1753326.1753503 · Cited by 416
A word at a time: computing word relatedness using temporal semantic analysis
Radinsky, K., Agichtein, E., Gabrilovich, E., & Markovitch, S. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963455 · Cited by 405
Metadata
- Authors
- Sahami, M., & Heilman, T. D.
- Venue
- Proceedings of the 15th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2006-05-23
- Pages
- 377-386
- OpenAlex
- W2161443453
- Cited by
- 735
- References
- 1
- FWCI
- 31.29
- 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: A web-based kernel function for measuring the similarity of short text snippets. https://app.cochat.ai/papers/10.1145/1135777.1135834
- The paper
- Sahami, M., & Heilman, T. D. (2006). A web-based kernel function for measuring the similarity of short text snippets. Proceedings of the 15th international conference on World Wide Web. https://doi.org/10.1145/1135777.1135834
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.
