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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 |
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container_title | Information sciences |
container_volume | 188 |
creator | Ferjani, Fethi Elloumi, Samir Jaoua, Ali Ben Yahia, Sadok Ismail, Sahar Ravan, Sheikha |
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 |
format | article |
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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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