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Formal context coverage based on isolated labels: An efficient solution for text feature extraction

Different available data as images, texts, or database may be mapped into an equivalent or approximate binary relation. A text may be considered as a binary relation relating sentences to words, while a numerical table may be represented by a binary relation after using some scaling approach. A soci...

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Published in:Information sciences 2012-04, Vol.188, p.198-214
Main Authors: Ferjani, Fethi, Elloumi, Samir, Jaoua, Ali, Ben Yahia, Sadok, Ismail, Sahar, Ravan, Sheikha
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cited_by cdi_FETCH-LOGICAL-c364t-17f890c2d61a76c66117a18e58eb61101702330002879b16ae73b8660ead5053
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container_title Information sciences
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creator Ferjani, Fethi
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description Different available data as images, texts, or database may be mapped into an equivalent or approximate binary relation. A text may be considered as a binary relation relating sentences to words, while a numerical table may be represented by a binary relation after using some scaling approach. A social network may be also represented by a formal context. The objective of this paper is to present an original approach for covering a binary relation by formal concepts based on isolated single or multiple properties, i.e., those belonging to only one concept. As a matter of fact, isolated properties are efficiently used for discriminating and labeling concepts. The latter are used for browsing in a corpora, or in a document by navigating through associated labels. By using fringe relations, the presented approach compared to those of the literature has the advantage of offering a relevant feature of a context by significant labels. Carried out experiments show the benefits of the introduced approach.
doi_str_mv 10.1016/j.ins.2011.10.023
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identifier ISSN: 0020-0255
ispartof Information sciences, 2012-04, Vol.188, p.198-214
issn 0020-0255
1872-6291
language eng
recordid cdi_hal_primary_oai_HAL_hal_01300495v1
source ScienceDirect Journals
subjects Approximation
Automatic indexing
Computer Science
Data Structures and Algorithms
Formal context analysis
Fringe relation
Isolated labels
Labeling
Labels
Minimal coverage
Sentences
Social networks
Tables (data)
Texts
title Formal context coverage based on isolated labels: An efficient solution for text feature extraction
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