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Pattern Recognition in Embedded Systems: An Overview Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Multilingual Documents Clustering Based on Closed Concepts Mining

Authors : Mohamed Chebel, Chiraz Latiri, Eric Gaussier

Published in: Database and Expert Systems Applications

Publisher: Springer International Publishing

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Abstract

The scarcity of bilingual and multilingual parallel corpora has prompted many researchers to accentuate the need for new methods to enhance the quality of comparable corpora. In this paper, we highlight the interest and usefulness of Formal Concept Analysis in multiligual document clustering to improve corpora comparability. We propose a statistical approach for clustering multiligual documents based on multilingual Closed Concepts Mining to partition the documents belonging to one or more collections, writing in more than one language, in a set of classes. Experimental evaluation was conducted on two collections and showed a significant improvement of comparability of the generated classes.

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previous chapter A Prime Number Based Approach for Closed Frequent Itemset Mining in Big Data
next chapter Analyzing the Strength of Co-authorship Ties with Neighborhood Overlap
Footnotes
1
In this paper, we denote by |X| the cardinality of the set X.
 
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Metadata
Title
Multilingual Documents Clustering Based on Closed Concepts Mining
Authors
Mohamed Chebel
Chiraz Latiri
Eric Gaussier
Copyright Year
2015
Book
Database and Expert Systems Applications

Print ISBN: 978-3-319-22848-8

Electronic ISBN: 978-3-319-22849-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-22849-5_36

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