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2 edition of comparison of two methods for boolean query relevance feedback found in the catalog.

comparison of two methods for boolean query relevance feedback

Gerard Salton

comparison of two methods for boolean query relevance feedback

by Gerard Salton

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  • 21 Currently reading

Published by Dept. of Computer Science, Cornell University in Ithaca, N.Y .
Written in English

    Subjects:
  • Information retrieval -- Automation.,
  • Online bibliographic searching.

  • Edition Notes

    StatementG. Salton, E.A. Fox, E. Voorhees.
    SeriesTechnical report / Dept. of Computer Science, Cornell University -- TR 83-564, Technical report (Cornell University. Dept. of Computer Science) -- TR 83-564
    ContributionsFox, Edward A. 1950-, Voorhees, E.
    The Physical Object
    Pagination28 p. --
    Number of Pages28
    ID Numbers
    Open LibraryOL18997561M

    The dramatic growth of the Internet has created a new problem for users: location of the relevant sources of documents. This article presents a framework for (and experimentally analyzes a solution to) this problem, which we call the text-source discovery approach consists of two phases. There are two key di erences between pseudo-relevance feedback and our approach: First, instead of adding terms blindly, we provide the user with sug-gestions for query adaptation. Second, we deal with Boolean queries, which implies that we do not have a relevance ranking .

    Background. A number of techniques have been researched over the last 40 years in order to improve retrieval effectiveness including relevance ranking, query expansion, and relevance feedback in both the biomedical domain as well as other subject domains. 1–3 The work presented here examines one specific influential technique: relevance ranking as part of an ongoing investigation of how to. FIG. 5. System comparison portion of questionnaire for comparing SWORD and BOOLEAN (a), SAPHIRE and BOOLEAN (b). and SAPHIRE and SWORD(c). Anulysis ofSearch Results User searching performance was assessed by calculat- ing the recall and precision for each system at two differ- ent levels of relevance.

    1. Lancaster, F.W. and Fayen, E.G. Information Retrieval On-Line. Melville Publishing Co., Los Angeles, California, Google Scholar; 2. Lancaster, F.W. Boolean Models Problems • Very rigid: AND means all; OR means any. • Difficult to express complex user requests. • Difficult to control the number of documents retrieved. – All matched documents will be returned. • Difficult to rank output. – All matched documents logically satisfy the query. • Difficult to perform relevance feedback.


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Comparison of two methods for boolean query relevance feedback by Gerard Salton Download PDF EPUB FB2

Finally a new Boolean query is constructed by taking the disjunction of the terms in each category, either singly, if included in the highest prevalence A comparison of two methods for Boolean query relevance feedback category (prevr >_ floor 1), or as and pairs if the terms are included in the second highest prevalence category (floor 1 > prey >_ floor 2), or as anded triples if the terms are in Cited by: Evaluates and compares two recently proposed automatic methods for relevance feedback of Boolean queries (Dillon method, which uses probabilistic approach as basis, and disjunctive normal form method).

Conclusions are drawn concerning the use of effective feedback methods in a Boolean query environment. Nineteen references are included. (EJS)Cited by: A comparison of two methods for Boolean query relevance feedback In this study two recently proposed automatic methods for relevance feedback of Boolean.

Home Browse by Title Periodicals Information Processing and Management: an International Journal Vol. 42, No. 2 Adaptive relevance feedback method of extended Boolean model using hierarchical clustering techniquesAuthor: ChoiJongpill, KimMinkoo, V RaghavanVijay.

Relevance feedback methods in extended Boolean model. A number of attempts have been made in the past to develop adequate methods for an extended Boolean query relevance feedback (Dillon et al.,Lose,Salton et al.,Salton et al., ).

Most methods consisted of two by: 9. Local methods adjust a query relative to the documents that initially appear to match the query. The basic methods here are: Relevance feedback (Section ) Pseudo relevance feedback, also known as Blind relevance feedback (Section ) (Global) indirect relevance feedback (Section ) In this chapter, we will mention all of these.

G. Salton, E. Fox, E. VoorheesA comparison of two methods for Boolean query relevance feedback Dept. of Computer Science, Cornell University, Ithaca, NY () Google Scholar. This user feedback is used by the Smart Boolean system to automatically modify the Boolean Topic representation or the query surrogate, whatever is more appropriate.

The Smart Boolean approach offers a rich set of strategies for modifying a query based on the received relevance feedback or the expressed need to narrow or broaden the query.

(a) (b) Relevance feedback searching over images.(a) The user views the initial query results for a query of bike, selects the first, third and fourth result in the top row and the fourth result in the bottom row as relevant, and submits this feedback.

The relative performance of variant relevance feedback methods can be validly compared, but it is difficult to validly compare performance with and without relevance feedback because the collection size and the number of relevant documents changes from before the feedback to after it.

Thus neither of these methods is fully satisfactory. Comparing Boolean and Probabilistic Information Retrieval Systems Across Queries and Disciplines two processes. The first, the query, provides some information external to the query by the query is examined in Losee [Los88]. The second way that parameters’ values may be estimated is through relevance feedback, information provided by.

The idea of modeling search as a conversation has been around for decades. One of the oldest ideas in information retrieval is relevance feedback, which dates back to the nce feedback allows searchers to tell the search engine which results are and aren’t relevant, guiding the search engine better understand the query and thus improve the results.

G. Salton, E.A. Fox, E. VoorheesA comparison of two methods for Boolean query relevance feedback Information Processing & Management, 20. In the relevance feedback method for the extended Boolean model, an initial Boolean query is reformulated in a DNF (Disjunctive Normal For m) f or mula based on users’ relevance feedbacks.

Not. In this paper we investigate whether a better Boolean query than that defined in the protocol of a systematic review, can be created, and we develop methods for the transformation of a given.

Donna Harman. Information retrieval (IR) is concerned with the structure, analysis, organization, storage, searching, and dissemination of information. An IR system is designed to make a given.

Since {d 3, d 4, d 5} ∩ {d 2} = ∅, no documents are retrieved for this query. Next consider the Boolean query: learning OR pedagogy. The Boolean OR operator corresponds to the classical set union operation. The documents in the postings list for the term learning are {d 2, d 4, d 5} and the same for the term pedagogy are {d 3, d 4, d 5}.

Abstract. Query expansion is a long standing relevance feedback technique for improving the effectiveness of information retrieval systems. Previous investigations have shown it to be generally effective for electronic text, to give proportionally better improvement for automatic transcriptions of spoken documents, and to be at best of questionable utility for optical character.

Relevance Feedback Main Idea: • Modify existing query based on relevance judgements •Extract terms from relevant documents and add them to the query •AND/OR re-weight the terms already in the query There are many variations: • Usually positive weights for terms from relevant docs • Sometimes negative weights for terms from non.

Legal Query Expansion using Ontologies and Relevance Feedback Erich Schweighofer, Anton Geist Centre for Computers and Law Section for International Law and International Relations University of Vienna, Austria Abstract.

The aim of our research is the improvement of Boolean search with query expansion using lexical ontologies and user feedback.

When comparing two methods, it is unfair to allow one method to fix a parameter that the other method will be evaluated over. In this case, it is the parameter B, the number of documents the Boolean query retrieved.

This is obviously the ideal point for the Boolean query to be evaluated at; all other points are non-optimal and have technical.This study investigates the use and effectiveness of an advanced information retrieval (IR) system (IN-QUERY). 64 novice IR system users were studied in their use of a baseline version of INQUERY compared with one of three experimental versions, each offering a different level of interaction with a relevance feedback facility for automatic query reformulation.Blind query expansion (also known as automatic relevance feedback) is enabled by adding WITH QUERY EXPANSION or IN NATURAL LANGUAGE MODE WITH QUERY EXPANSION following the search phrase.

It works by performing the search twice, where the search phrase for the second search is the original search phrase concatenated with the few most highly.