Classification of Brand Names Based on n-Grams

Pattaraporn Warintarawej 1 Anne Laurent 1, * Pierre Pompidor 1 Benedicte Laurent 2
* Auteur correspondant
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Supervised classification has been extensively addressed in the literature as it has many applications, especially for text categorization or web content mining where data are organized through a hierarchy. On the other hand, the automatic analysis of brand names can be viewed as a special case of text management, although such names are very different from classical data. They are indeed often neologisms, and cannot be easily managed by existing NLP tools. In our framework, we aim at automatically analyzing such names and at determining to which extent they are related to some concepts that are hierarchically organized. The system is based on the use of character n-grams. The targeted system is meant to help, for instance, to automatically determine whether a name sounds like being related to ecology.
Type de document :
Communication dans un congrès
SoCPaR'10: IEEE Conference on Soft Computing and Pattern Recognition, Dec 2010, France. pp.6, 2010, 〈http://www.mirlabs.org/socpar10/〉. 〈10.1109/SOCPAR.2010.5685842〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00582626
Contributeur : Pattaraporn Warintarawej <>
Soumis le : samedi 2 avril 2011 - 18:17:03
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

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Pattaraporn Warintarawej, Anne Laurent, Pierre Pompidor, Benedicte Laurent. Classification of Brand Names Based on n-Grams. SoCPaR'10: IEEE Conference on Soft Computing and Pattern Recognition, Dec 2010, France. pp.6, 2010, 〈http://www.mirlabs.org/socpar10/〉. 〈10.1109/SOCPAR.2010.5685842〉. 〈lirmm-00582626〉

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