Document (#25036)

Author

Ponte, J.M.

Title

Language models for relevance feedback

Source

Advances in information retrieval: Recent research from the Center for Intelligent Information Retrieval. Ed.: W.B. Croft

Imprint

Boston, MA : Kluwer Academic Publ.

Year

2000

Pages

S.73-95

Series

The Kluwer international series on information retrieval; 7

Abstract

The language modeling approach to Information Retrieval (IR) is a conceptually simple model of IR originally developed by Ponte and Croft (1998). In this approach, the query is treated as a random event and documents are ranked according to the likelihood that the query would be generated via a language model estimated for each document. The intuition behind this approach is that users have a prototypical document in mind and will choose query terms accordingly. The intuitive appeal of this method is that inferences about the semantic content of documents do not need to be made resulting in a conceptually simple model. In this paper, techniques for relevance feedback and routing are derived from the language modeling approach in a straightforward manner and their effectiveness is demonstrated empirically. These experiments demonstrate further proof of concept for the language modeling approach to retrieval

Theme

Computerlinguistik
Retrievalalgorithmen

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5. Liu, X.; Croft, W.B.: Statistical language modeling for information retrieval (2004) 0.30

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