Using Probabilistic Models of Document Retrieval Without Relevance Information

Croft, W. Bruce; Harper, David J.

doi:10.1108/eb026683

Cited by 378 publications

(188 citation statements)

References 4 publications

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“…Indeed, the BIR model has encountered difficulties in estimating p (t | Q, r) and p (t | Q, r) when no explicit relevance information is available. Typically, p (t | Q, r) is set to a constant and p (t | Q, r) is estimated under the assumption that the each document in the collection is not relevant (Croft and Harper, 1979;Robertson and Walker, 1997). Recently, Lavrenko and Croft made progress in estimating the rel-evance model without relevance judgments by exploiting language modeling techniques (Lavrenko and Croft, 2001).…”

Section: Probabilistic Relevance Modelsmentioning

confidence: 99%

A risk minimization framework for information retrieval

Zhai

Lafferty

2006

Information Processing & Management

119

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Section: Probabilistic Relevance Modelsmentioning

confidence: 99%

A risk minimization framework for information retrieval

Zhai

Lafferty

2006

Information Processing & Management

119

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“…They assumed that the top few documents retrieved by an initial run were relevant, in the absence of any real relevance judgements, and considered all the terms contained in such documents as candidates for query reweighting, with (Attar and Fraenkel 1977) or without (Croft and Harper 1979) query expansion.…”

Section: Retrieval Feedback Techniquesmentioning

confidence: 99%

Improving retrieval feedback with multiple term-ranking function combination

Carpineto

Romano

Giannini

2002

ACM Trans. Inf. Syst.

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and VITTORIO GIANNINI WIND Telecomunicazioni S.p.A ________________________________________________________________________In this paper we consider methods for automatic query expansion from top retrieved documents (i.e., retrieval feedback) which make use of various functions for scoring expansion terms within Rocchio's classical reweighting scheme. An analytical comparison shows that the retrieval performance of methods based on distinct term-scoring functions is comparable on the whole query set but considerably differs on single queries, consistent with the fact that the ordered sets of expansion terms suggested for each query by the different functions are largely uncorrelated. Motivated by these findings, we argue that the results of multiple functions can be merged, by analogy with ensembling classifiers, and present a simple combination technique based on the rank values of the suggested terms. The combined retrieval feedback method is effective not only with respect to unexpanded queries but also to any individual method, with notable improvements on the system's precision. Furthermore, the combined method is robust with respect to variation of experimental parameters and it is beneficial even when the same information needs are expressed with shorter queries.

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“…Their goal was to measure for each document the probability that the document will satisfy a given request for information. Other well known probabilistic models are those of Robertson and Sparck-Jones (1976), Croft and Harper (1979), Fuhr (1989) and Turtle and Croft (1991). These all share certain properties.…”

Section: Retrieval Models and Structured Query Translationmentioning

confidence: 99%

Structured queries, language modeling, and relevance modeling in cross-language information retrieval

Larkey

Connell

2005

Information Processing & Management

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Two probabilistic approaches to cross-lingual retrieval are in wide use today, those based on probabilistic models of relevance, as exemplified by INQUERY, and those based on language modeling. INQUERY, as a query net model, allows the easy incorporation of query operators, including a synonym operator, which has proven to be extremely useful in cross-language information retrieval (CLIR), in an approach often called structured query translation. In contrast, language models incorporate translation probabilities into a unified framework. We compare the two approaches on Arabic and Spanish data sets, using two kinds of bilingual dictionaries -one derived from a conventional dictionary, and one derived from a parallel corpus. We find that structured query processing gives slightly better results when queries are not expanded. On the other hand, when queries are expanded, language modeling gives better results, but only when using a probabilistic dictionary derived from a parallel corpus.We pursue two additional issues inherent in the comparison of structured query processing with language modeling. The first concerns query expansion, and the second is the role of translation probabilities. We compare conventional expansion techniques (pseudo-relevance feedback) with relevance modeling, a new IR approach which fits into the formal framework of language modeling. We find that relevance modeling and pseudo-relevance feedback achieve comparable levels of retrieval and that good translation probabilities confer a small but significant advantage.

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Using Probabilistic Models of Document Retrieval Without Relevance Information

Cited by 378 publications

References 4 publications

A risk minimization framework for information retrieval

A risk minimization framework for information retrieval

Improving retrieval feedback with multiple term-ranking function combination

Structured queries, language modeling, and relevance modeling in cross-language information retrieval

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