Enhancing the Set-Based Model Using Proximity Information

Pôssas, Bruno; Ziviani, Nívio; Meira, Wagner

doi:10.1007/3-540-45735-6_11

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…Term proximity has been explored extensively in document ranking studies [16,17,18,19,20], where several distance factors were proposed. Two common intuitions underlie these approaches: (1) the closer the terms are in a document, the more likely they are topically related, and (2) the closer the query terms are in a document, the more likely the document is relevant to the query.…”

Section: Introductionmentioning

confidence: 99%

A study of the effect of term proximity on query expansion

Vechtomova

Wang²

2006

Journal of Information Science

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Query expansion terms are often used to enhance original query formulations in document retrieval. Such terms are usually selected from the entire documents or from windows or passages surrounding query term occurrences. Arguably, the semantic relatedness between terms weakens with the increase in the distance separating them. In this paper we report a study that was conducted to systematically evaluate different distance functions for selecting query expansion terms. We propose a distance factor that can be effectively combined with the statistical term association measure of Mutual Information for selecting query expansion terms. Evaluation on TREC collection shows that the distance-weighted Mutual Information is more effective than Mutual Information alone in selecting terms for query expansion.

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Section: Introductionmentioning

confidence: 99%

A study of the effect of term proximity on query expansion

Vechtomova

Wang²

2006

Journal of Information Science

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“…The proposal presented by the authors in [13] is similar with [14]. Then, in [12], an extension to the set-based model is proposed using information about proximity among the terms of the query in the documents.…”

Section: Related Workmentioning

confidence: 96%

“…An extension to the Vector space model was suggested by Possas et al in [12,13,14] considering the correlation among the terms, obtained using association rules. In [14], a new model is presented, denominated set-based model, for computing term weights, based on set theory, and for ranking documents.…”

Section: Related Workmentioning

confidence: 99%

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Dependence among terms in vector space model

Silva

Souza

Santos

Proceedings. International Database Engineering and Applications Symposium, 2004. IDEAS '04.

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The vector space model is a mathematical-based model that represents terms, documents and queries by vectors and provides a ranking. In this model, the subspace of interest is formed by a set of pairwaise orthogonal term vectors, indicating that terms are mutually independent. However, this is a simplification that doesn't correspond to the reality. Based on this scenery, we present, in this work, an extension to the vector space model to take into account the correlation between terms. In the proposed model, term vectors are rotated in space geometrically reflecting the dependence semantics among terms. We rotate terms based on a data mining technique called association rules. The retrieval effectiveness of the proposed model is evaluated and the results shows that our model improves in average precision, relative to the standard vector space model, for all collections evaluated, leading to a gain up to 31%.

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“…Analogamente, o vetor para a consulta q c definido por: [23,24,25] , rnor"q de associação são consideradas úteis se elas satisfazem um hotéis. Tipicamente, regras ue as y limiar de suporte c confiança mínimos [15].…”

Section: Trabalhos Relacionadosunclassified

Dependência entre termos no modelo vetorial

Santos¹

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The number of available electronic Information for access in digital libraries and Web is growing fast. An immediate consequence is that lhe task of finding useful information becomes difficult. Improving upon this situation requires progresses in the project and implemenlation of information retrieval systems, among them, ranking algorithms. The Vector Space Model is an approach, which has been used along the years to provide such ranking. In this model, each index term corresponds to a vector, and these vectors, together, generatc the basis of the vector space of interest. In this basis, the vectors are pairwaise orthogonal, indicating that the corresponding terms are mutually independent. However, this simplification does not correspond to the reality. Then, we present, in this work, an extension to the Vector Model to take into account the correlation between terms. In the proposed model, term vectors, originally orthogonal, are rotated in space geometrically reflecting the dependence semantics among terms. This rotation is done with any technique that generates information on the relationship among terms of the collection. We propose two techniques, named, association rules and the generation of terms lexicographically similar. The generation of association rules is a known data mining technique. It is used in the information retrieval to fínd sets of terms that co-occur in documents collection. The technique of obtaining terms lexicographically similar creatures is a strategy similar to the extraction of radicais. The retrieval effectiveness of the proposed model is evaluated for the two techniques using the measures of precision and recall. The results shows that our model improves in average precision, relative to the standard Vector Model, for all collections evaluated, leading to a gain up to 31%.

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Enhancing the Set-Based Model Using Proximity Information

Cited by 6 publications

References 19 publications

A study of the effect of term proximity on query expansion

A study of the effect of term proximity on query expansion

Dependence among terms in vector space model

Dependência entre termos no modelo vetorial

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