Weighting, ranking and relevance feedback in a front—end system

Robertson, Stephen; Thompson, C L; Macaskill, M J; Bovey, J. D.

doi:10.1177/016555158601200112

Cited by 23 publications

(8 citation statements)

References 6 publications

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“…The other method to perform query expansion, perhaps the most effective of all, is that of relevance feedback [11]. In this method the user submits a query, which capitulate an initial set of results.…”

Section: Query Expansionmentioning

confidence: 99%

Query Expansion using Artificial Relevance Feedback

Joshi¹,

Kushwaha²

2012

IJCA

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World Wide Web is growing rapidly so with this rapid expansion in the size of web, Information extraction on web is achieving its importance day by day. The user's query[1] plays a crucial role in the information retrieval process. So for the better information retrieval[2] results several methods have been proposed which help the user in the query expansion task. Some methods use thesaurus for the query expansion purpose. Thesaurus is nothing but a list of synonyms. Latest techniques for query expansion are mining user logs and creating user profiles. In the proposed system we present query expansion using Artificial Relevance Feedback Mechanism. The proposed system provides a simple way of query expansion based on Artificial Relevance Feedback.

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Section: Query Expansionmentioning

confidence: 99%

Query Expansion using Artificial Relevance Feedback

Joshi¹,

Kushwaha²

2012

IJCA

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“…One example described in Robertson, Thompson and Macaskill (1986) required a very large sample of independent searches (e.g., 500 topics). A relatively small number of search topics was usually used in a matched-pair design study (between two and eight), while a non-matched-pair design required a very large number of search topics.…”

Section: Search Topic Variabilitymentioning

confidence: 99%

The impact of MeSH (Medical Subject Headings) terms on information seeking effectiveness

Liu

2009

SIGIR Forum

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To what extent do MeSH (Medical Subject Headings) terms improve search effectiveness of different kinds of users? We observed four different kinds of searchers using an experimental information retrieval (IR) system: (1) search novices; (2) domain experts; (3) search experts and (4) medical librarians. The information needs were a subset of the relatively difficult topics originally created for the Text REtrieval Conference (TREC). By experimental design, we used 20 search topics in an IR user experiment to alleviate search topic variability. Effectiveness of retrieval was based on the relevance judgments set provided by TREC. Thirty-two participants searched either using a version of the system in which abstracts and MeSH terms were displayed or another version in which they had to formulate their own terms based only on the display of abstracts. We found that MeSH terms were more useful for domain experts than for search experts in terms of the precision measure, even though domain experts did not perceive that MeSH terms were useful. We speculate that because of the technical topics, only the domain experts had the knowledge to understand and therefore make use of the MeSH terms. The primary contributions of this research are: (1) assessment of relative impact of searchers characteristics of domain knowledge and search training on search effectiveness and (2) design and methodology for assessing the usefulness of controlled vocabulary. The effort to create MeSH terms is worthwhile for domain experts' searches on technical topics.

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“…The information provided by the query is examined in Losee ( 1988 ). The second way that parameters' values may be estimated is through relevance feedback, information provided by the searcher about what the user finds to be of interest (Bookstein, 1983;Chow & Yu, 1982;Losee, 1988;Moon, 1993;Robertson, Thompson, Macaskill, & Bovey, 1986;Smeaton, 1984;Spink, 1995 ). When estimating probabilities for use in the probabilistic model, it is necessary to either assume statistical independence of terms or to formally incorporate some form of statistical dependence between docu-ment features (Chow & Liu, 1968;Cooper, 1995;Croft, 1986;Lam & Yu, 1982;Losee, 1994aLosee, , 1995aVan Rijsbergen, 1977;Yu, Buckley, Lam, & Salton, 1983 ).…”

Section: Models Of Retrieval and Termmentioning

confidence: 99%

Comparing Boolean and probabilistic information retrieval systems across queries and disciplines

Losee¹

1997

J. Am. Soc. Inf. Sci.

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Whether using Boolean queries or ranking documents using document and term weights will result in better retrieval performance has been the subject of considerable discussion among document retrieval system users and researchers. We suggest a method that allows one to analytically compare the two approaches to retrieval and examine their relative merits. The performance of information retrieval systems may be determined either by using experimental simulation, or through the application of analytic techniques that directly estimate the retrieval performance, given values for query and database characteristics. Using these performance predicting techniques, sample performance figures are provided for queries using the Boolean and or, as well as for probabilistic systems assuming statistical term independence or term dependence. The variation of performance across sublanguages (used in different academic disciplines) and queries is examined. The performance of models failing to meet statistical and other assumptions is examined. © 1997 John Wiley & Sons, Inc.

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Weighting, ranking and relevance feedback in a front—end system

Cited by 23 publications

References 6 publications

Query Expansion using Artificial Relevance Feedback

Query Expansion using Artificial Relevance Feedback

The impact of MeSH (Medical Subject Headings) terms on information seeking effectiveness

Comparing Boolean and probabilistic information retrieval systems across queries and disciplines

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