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A web-based kernel function for measuring the similarity of short text snippets

by: M. Sahami, and T. D. Heilman

In: WWW '06: Proceedings of the 15th international conference on World Wide Web New York, NY, USA: ACM (2006) , p. 377--386.

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URL:	http://portal.acm.org/citation.cfm?id=1135834
DOI:	10.1145/1135777.1135834

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.

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A web-based kernel function for measuring the similarity of short text snippets

BibTeX record

@inproceedings{sahami2006,
  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.},
  added-at = {2009-08-19T02:14:21.000+0200},
  address = {New York, NY, USA},
  author = {Sahami, M. and Heilman, T. D.},
  biburl = {http://www.bibsonomy.org/bibtex/22a2680a4184f22662c2a35a5a6bae25c/mstrohm},
  booktitle = {WWW '06: Proceedings of the 15th international conference on World Wide Web},
  description = {A web-based kernel function for measuring the similarity of short text snippets},
  doi = {http://doi.acm.org/10.1145/1135777.1135834},
  interhash = {ed02f66d2bf7e17397c9b7de4c90e2dc},
  intrahash = {2a2680a4184f22662c2a35a5a6bae25c},
  isbn = {1-59593-323-9},
  keywords = {SEMINAL information-retrieval similarity},
  location = {Edinburgh, Scotland},
  pages = {377--386},
  publisher = {ACM},
  timestamp = {2009-08-19T02:14:21.000+0200},
  title = {A web-based kernel function for measuring the similarity of short text snippets},
  url = {http://portal.acm.org/citation.cfm?id=1135834},
  year = 2006
}

Endnote record

%0 Conference Paper
%1 sahami2006
%A Sahami, M.
%A Heilman, T. D.
%B WWW '06: Proceedings of the 15th international conference on World Wide Web
%@ 1-59593-323-9
%C New York, NY, USA
%D 2006
%I ACM
%K 
%P 377--386
%T A web-based kernel function for measuring the similarity of short text snippets
%R http://doi.acm.org/10.1145/1135777.1135834
%U http://portal.acm.org/citation.cfm?id=1135834
%X 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.

BibSonomy :: publication ::

A web-based kernel function for measuring the similarity of short text snippets

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