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Conference papers

Dimensionnalité intrinsèque dans les espaces de représentation des termes et des documents

Vincent Claveau 1, 2
Abstract : Examining the properties of representation spaces for documents or words in IR (typically R n with n large) brings precious insights to help the retrieval process. Recently, several authors have studied the real dimensionality of the datasets, called intrinsic dimensionality, in specific parts of these spaces (Houle et al., 2012a). In this paper, we propose to revisit this notion through a coefficient called α in the specific case of IR and to study its use in IR tasks. More precisely, we show how to estimate α from IR similarities and to use it in representation spaces used for documents and words (Mikolov et al., 2013 ; Claveau et al., 2014). Indeed, we prove that α may be used to characterize difficult queries; moreover we show that this intrinsic dimensionality notion, applied to words, can help to choose terms to use for query expansion.
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Submitted on : Wednesday, November 9, 2016 - 4:51:55 PM
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Vincent Claveau. Dimensionnalité intrinsèque dans les espaces de représentation des termes et des documents. Conférence en Recherche d'Information et Applications, CORIA, Mar 2016, Toulouse, France. ⟨hal-01394749⟩

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