2017 AStudyofSmoothingMethodsforLang

• (Zhai & Lafferty, 2017) ⇒ Chengxiang Zhai, and John Lafferty. (2017). “A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval.” In: ACM SIGIR Forum Journal, 51(2). doi:10.1145/3130348.3130377

Subject Headings: Maximum-likelihood language models.

Cited By

• http://scholar.google.com/scholar?q=%222017%22+A+Study+of+Smoothing+Methods+for+Language+Models+Applied+to+Ad+Hoc+Information+Retrieval
• http://dl.acm.org/citation.cfm?id=3130348.3130377&preflayout=flat#citedby

Quotes

Abstract

Language modeling approaches to information retrieval are attractive and promising because they connect the problem of retrieval with that of language model estimation, which has been studied extensively in other application areas such as speech recognition. The basic idea of these approaches is to estimate a language model for each document, and then rank documents by the likelihood of the query according to the estimated language model. A core problem in language model estimation is smoothing, which adjusts the maximum likelihood estimator so as to correct the inaccuracy due to data sparseness. In this paper, we study the problem of language model smoothing and its influence on retrieval performance. We examine the sensitivity of retrieval performance to the smoothing parameters and compare several popular smoothing methods on different test collection.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 AStudyofSmoothingMethodsforLang	John D. Lafferty ChengXiang Zhai			A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval				10.1145/3130348.3130377		2017