Skip to Main content Skip to Navigation
Conference papers

Using Global Statistics to Rank Retrieval Systems without Relevance Judgments

Abstract : How to reduce the amount of relevance judgments is an important issue in retrieval evaluation. In this paper, we propose a novel method using global statistics to rank retrieval systems without relevance judgments. In our method, a series of global statistics of a system, which indicate the percentage of its documents found by k out of all the N systems (k = 1, 2, ..., N), are selected, then a linear combination of the series of global statistics is utilized to fit the mean average precision (MAP) of the retrieval system. Optimal coefficients are obtained by linear regression. No human relevance judgments are required in the entire process. Compared with existing evaluation methods without relevance judgments, our method has two advantages. Firstly, it outperforms all early attempts. Secondly, it is adjustable for different effectiveness measurements, e.g. MAP, precision at n, and so forth.
Document type :
Conference papers
Complete list of metadata

Cited literature [12 references]  Display  Hide  Download

https://hal.inria.fr/hal-01055060
Contributor : Hal Ifip <>
Submitted on : Monday, August 11, 2014 - 12:58:55 PM
Last modification on : Thursday, March 5, 2020 - 5:43:12 PM
Long-term archiving on: : Wednesday, November 26, 2014 - 9:56:44 PM

File

Using_Global_Statistics_to_Ran...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Zhiwei Shi, Bin Wang, Peng Li, Zhongzhi Shi. Using Global Statistics to Rank Retrieval Systems without Relevance Judgments. 6th IFIP TC 12 International Conference on Intelligent Information Processing (IIP), Oct 2010, Manchester, United Kingdom. pp.183-192, ⟨10.1007/978-3-642-16327-2_24⟩. ⟨hal-01055060⟩

Share

Metrics

Record views

346

Files downloads

292