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A Generative Theory of RelevanceRetrieval Scenarios

A Generative Theory of Relevance: Retrieval Scenarios [In the previous chapters we tried to keep our definitions very abstract &$x2014; talking about conceptual “representation” spaces, “transform” functions and unspecified “parameter” vectors. Our motivation was to keep the model as general as possible, so it would be applicable to a wide range of retrieval problems. Now is the time to bring our discussion down to earth and provide specific definitions for a number of popular retrieval scenarios. We will discuss the following retrieval scenarios: Ad-hoc retrieval: we have a collection of English documents, and a short English query. The goal is to retrieve documents relevant to the query.Relevance feedback: in addition to the query, the user provides us with a few examples of relevant documents. The goal is to retrieve more relevant documents.Cross-language retrieval: we have a collection of Chinese documents and an English query. The goal is to find Chinese relevant documents.Handwriting retrieval: we have a set of historical manuscripts, represented as bitmap images. The goal is to search the collection using text queries.Image retrieval: we have a collection of un-labeled photographs. The goal is to identify photographs relevant to a given text query (e.g., find “tiger in the grass”).Video retrieval: we have a collection of un-annotated video footage. The goal is to find video shots containing objects of interest (e.g., “forest fire”).Structured search with missing data: we have a database with missing field values in many records. The goal is to satisfy structured queries in the face of incomplete data.Topic detection and tracking: we have a live stream of news reports. The goal is to organize the reports according to the events discussed in them.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Generative Theory of RelevanceRetrieval Scenarios

Part of the The Information Retrieval Series Book Series (volume 26)
Springer Journals — Jan 1, 2009
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72 pages

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Publisher
Springer Berlin Heidelberg
Copyright
© Springer Berlin Heidelberg 2009
ISBN
978-3-540-89363-9
Pages
103 –174
DOI
10.1007/978-3-540-89364-6_5
Publisher site
See Chapter on Publisher Site

Abstract

[In the previous chapters we tried to keep our definitions very abstract &$x2014; talking about conceptual “representation” spaces, “transform” functions and unspecified “parameter” vectors. Our motivation was to keep the model as general as possible, so it would be applicable to a wide range of retrieval problems. Now is the time to bring our discussion down to earth and provide specific definitions for a number of popular retrieval scenarios. We will discuss the following retrieval scenarios: Ad-hoc retrieval: we have a collection of English documents, and a short English query. The goal is to retrieve documents relevant to the query.Relevance feedback: in addition to the query, the user provides us with a few examples of relevant documents. The goal is to retrieve more relevant documents.Cross-language retrieval: we have a collection of Chinese documents and an English query. The goal is to find Chinese relevant documents.Handwriting retrieval: we have a set of historical manuscripts, represented as bitmap images. The goal is to search the collection using text queries.Image retrieval: we have a collection of un-labeled photographs. The goal is to identify photographs relevant to a given text query (e.g., find “tiger in the grass”).Video retrieval: we have a collection of un-annotated video footage. The goal is to find video shots containing objects of interest (e.g., “forest fire”).Structured search with missing data: we have a database with missing field values in many records. The goal is to satisfy structured queries in the face of incomplete data.Topic detection and tracking: we have a live stream of news reports. The goal is to organize the reports according to the events discussed in them.]

Published: Jan 1, 2009

Keywords: Average Precision; Relevance Feedback; Relevance Model; Optical Character Recognition; Query Expansion

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