How Informative is a Term?: Dispersion as a measure of Term Specificity
Pages 853 - 856
Abstract
Similarity functions assign scores to documents in response to queries. These functions require as input statistics about the terms in the queries and documents, where the intention is that the statistics are estimates of the relative informativeness of the terms. Common measures of informativeness use the number of documents containing each term (the document frequency) as a key measure. We argue in this paper that the distribution of within-document frequencies across a collection is also pertinent to informativeness, a measure that has not been considered in prior work: the most informative words tend to be those whose frequency of occurrence has high variance. We propose use of relative standard deviation (RSD) as a measure of variability incorporating within-document frequencies, and show that RSD compares favourably with inverse document frequency (IDF), in both in-principle analysis and in practice in retrieval, with small but consistent gains.
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Index Terms
- How Informative is a Term?: Dispersion as a measure of Term Specificity
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Published In
July 2016
1296 pages
ISBN:9781450340694
DOI:10.1145/2911451
- General Chairs:
- Raffaele Perego,
- Fabrizio Sebastiani,
- Program Chairs:
- Javed Aslam,
- Ian Ruthven,
- Justin Zobel
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Published: 07 July 2016
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SIGIR '16
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SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval
July 17 - 21, 2016
Pisa, Italy
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SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%
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