Abstract
Measuring the similarity between documents and queries has been extensively studied in information retrieval. However, there are a growing number of tasks that require computing the similarity between two very short segments of text. These tasks include query reformulation, sponsored search, and image retrieval. Standard text similarity measures perform poorly on such tasks because of data sparseness and the lack of context. In this work, we study this problem from an information retrieval perspective, focusing on text representations and similarity measures. We examine a range of similarity measures, including purely lexical measures, stemming, and language modeling-based measures. We formally evaluate and analyze the methods on a query-query similarity task using 363,822 queries from a web search log. Our analysis provides insights into the strengths and weaknesses of each method, including important tradeoffs between effectiveness and efficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Berger, A., Lafferty, J.: Information retrieval as statistical translation. In: Proceedings of SIGIR ’99, pp. 222–229 (1999)
Cucerzan, S., Brill, E.: Extracting semantically related queries by exploiting user session information. Technical Report, Microsoft Research (2005)
Deerwester, S., et al.: Indexing by latent semantic analysis. JASIST 41(6), 391–407 (1990)
Jones, R.: Generating query substitutions. In: Proceedings of WWW 2006, pp. 387-396 (2006)
Krovetz, R.: Viewing morphology as an inference process. In: Proceedings of SIGIR ’93, pp. 191–202 (1993)
Lavrenko, V., Croft, W.B.: Relevance based language models. In: Proceedings of SIGIR ‘01, pp. 120-127 (2001)
Metzler, D., et al.: Similarity measures for tracking information flow. In: Proceedings of CIKM ‘05, pp. 517-524 (2005)
Murdock, V., Croft, W.B.: A Translation Model for Sentence Retrieval. In: Proceedings of HLT/EMNLP ‘05, pp. 684-691 (2005)
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Rocchio, J.J.: Relevance Feedback in Information Retrieval, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)
Sahami, M., Heilman, T.: A web-based kernel function for measuring the similarity of short text snippets. In: Proceedings of WWW 2006, pp. 377-386 (2006)
Strohman, T., et al.: Indri: A language model-based search engine for complex queries. In: Proceedings of the International Conference on Intelligence Analysis (2005)
Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: Proceedings of SIGIR ‘01, pp. 334-342 (2001)
Zhai, C., Lafferty, J.: Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of CIKM ‘01, pp. 403-410 (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Metzler, D., Dumais, S., Meek, C. (2007). Similarity Measures for Short Segments of Text. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-71496-5_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71494-1
Online ISBN: 978-3-540-71496-5
eBook Packages: Computer ScienceComputer Science (R0)