Effective reformulation of Boolean queries with concept lattices

Carpineto, Claudio; Romano, Giovanni

doi:10.1007/bfb0055993

Cited by 32 publications

(27 citation statements)

References 17 publications

(17 reference statements)

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“…A popular solution for multiple-query searches is to use manually [Brajnik et al 1996] or automatically built [Grefenstette 1994;Cooper and Byrd 1997;Carpineto and Romano 1998] thesauri for guiding the user to reformulate queries in an interactive process. A thesaurus can also be used for automatic query expansion on single-query searches, but doubts have been expressed about the benefits for the retrieval effectiveness in this case [Voorhees 1994].…”

Section: Introductionmentioning

confidence: 99%

An information-theoretic approach to automatic query expansion

Carpineto

Mori

Romano

et al. 2001

ACM Trans. Inf. Syst.

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287

218

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Techniques for automatic query expansion from top retrieved documents have shown promise for improving retrieval effectiveness on large collections; however, they often rely on an empirical ground, and there is a shortage of cross-system comparisons. Using ideas from Information Theory, we present a computationally simple and theoretically justified method for assigning scores to candidate expansion terms. Such scores are used to select and weight expansion terms within Rocchio's framework for query reweighting. We compare ranking with information-theoretic query expansion versus ranking with other query expansion techniques, showing that the former achieves better retrieval effectiveness on several performance measures. We also discuss the effect on retrieval effectiveness of the main parameters involved in automatic query expansion, such as data sparseness, query difficulty, number of selected documents, and number of selected terms, pointing out interesting relationships.

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Section: Introductionmentioning

confidence: 99%

An information-theoretic approach to automatic query expansion

Carpineto

Mori

Romano

et al. 2001

ACM Trans. Inf. Syst.

Self Cite

287

218

View full text Add to dashboard Cite

show abstract

“…The paucity of query terms exacerbates well known inherent limitations of information retrieval systems, such as the difficulty of recovering from word mismatch between queries and documents, and it may represent a fundamental practical limitation for effective retrieval from large databases. Much of the current research in information retrieval attempts to solve this problem by focusing on methods for the creation of a query "context" by using such diverse knowledge sources as user's relevance feedback (Harman, 1992), thesauri (Cooper and Byrd, 1997), and conceptual clustering of documents and terms (Carpineto and Romano, 1998), rather than concentrating on better ways of matching queries against documents.…”

Section: Introductionmentioning

confidence: 99%

Towards More Effective Techniques for Automatic Query Expansion

Carpineto

Romano

1999

Research and Advanced Technology for Digital Libraries

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Techniques for automatic query expansion from top retrieved documents have recently shown promise for improving retrieval effectiveness on large collections but there is still a lack of systematic evaluation and comparative studies. In this paper we focus on term-scoring methods based on the differences between the distribution of terms in (pseudo-)relevant documents and the distribution of terms in all documents, seen as a complement or an alternative to more conventional techniques. We show that when such distributional methods are used to select expansion terms within Rocchio's classical reweighting scheme, the overall performance is not likely to improve. However, we also show that when the same distributional methods are used to both select and weight expansion terms the retrieval effectiveness may considerably improve. We then argue, based on their variation in performance on individual queries, that the set of ranked terms suggested by individual distributional methods can be combined to further improve mean performance, by analogy with ensembling classifiers, and present experimental evidence supporting this view. Taken together, our experiments show that with automatic query expansion it is possible to achieve performance gains as high as 21.34% over non-expanded query (for non-interpolated average precision). We also discuss the effect that the main parameters involved in automatic query expansion, such as query difficulty, number of selected documents, and number of selected terms, have on retrieval effectiveness.

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“…These limitations can be overcome by using concept lattices. One example is the REFINER system [12].…”

Section: Query Refinementmentioning

confidence: 99%