CASIT: Content Based Identification of Textual Information in a Large Database

Guezouli, Larbi; Essafi, Hassane

doi:10.1109/waina.2010.133

Cited by 4 publications

(2 citation statements)

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“…where CASIT is a similarity measure between two textual documents. This measure is described in our previous publication [21], it takes into account the neighborhood of words. Therefore, instead of comparing two semi-structured documents we use the interference wave to calculate the rate of similarity between these documents.…”

Section: Interference Wavementioning

confidence: 99%

Search of Information Based Content in Semi-Structured Documents Using Interference Wave

Guezouli¹,

Essafi²

2016

IJCSITCE

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This paper proposes a semi-structured information retrieval model based on a new method for calculation of similarity. We have developed CASISS (Calculation of Similarity of Semi-Structured documents) method to quantify how two given texts are similar. This new method identifies elements of semi-structured documents using elements descriptors. Each semi-structured document is pre-processed before the extraction of a set of descriptors for each element, which characterize the contents of elements.It can be used to increase the accuracy of the information retrieval process by taking into account not only the presence of query terms in the given document but also the topology (position continuity) of these terms.

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Section: Interference Wavementioning

confidence: 99%

Search of Information Based Content in Semi-Structured Documents Using Interference Wave

Guezouli¹,

Essafi²

2016

IJCSITCE

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“…We proceed to compare PWCA to KCCA for a mate-retrieval task [13,14,15,16], i.e. given a document query q i in language x to retrieve the (exact) matching document in the paired language y.…”

Section: Experiments Of Pwca On Europalmentioning

confidence: 99%

Pair-Wise Cluster Analysis

Hardoon,

Pelcksman

2010

Preprint

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This paper studies the problem of learning clusters which are consistently present in different (continuously valued) representations of observed data. Our setup differs slightly from the standard approach of (co-) clustering as we use the fact that some form of 'labeling' becomes available in this setup: a cluster is only interesting if it has a counterpart in the alternative representation. The contribution of this paper is twofold: (i) the problem setting is explored and an analysis in terms of the PAC-Bayesian theorem is presented, (ii) a practical kernel-based algorithm is derived exploiting the inherent relation to Canonical Correlation Analysis (CCA), as well as its extension to multiple views. A content based information retrieval (CBIR) case study is presented on the multi-lingual aligned Europal document dataset which supports the above findings.

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CAS-based information retrieval in semi-structured documents: CASISS model

Guezouli

Essafi²

2016

Journal of Innovation in Digital Ecosystems

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CASIT: Content Based Identification of Textual Information in a Large Database

Cited by 4 publications

References 1 publication

Search of Information Based Content in Semi-Structured Documents Using Interference Wave

Search of Information Based Content in Semi-Structured Documents Using Interference Wave

Pair-Wise Cluster Analysis

CAS-based information retrieval in semi-structured documents: CASISS model

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