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Authors: Giuliano Armano ; Francesca Fanni and Alessandro Giuliani

Affiliation: University of Cagliari, Italy

ISBN: 978-989-758-074-1

Keyword(s): Stopwords, Discriminant Capability, Characteristic Capability, Text Classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: Stopwords are meaningless, non-significant terms that frequently occur in a document. They should be removed, like a noise. Traditionally, two different approaches of building a stoplist have been used: the former considers the most frequent terms looking at a language (e.g., english stoplist), the other includes the most occurring terms in a document collection. In several tasks, e.g., text classification and clustering, documents are typically grouped into categories. We propose a novel approach aimed at automatically identifying specific stopwords for each category. The proposal relies on two unbiased metrics that allow to analyze the informative content of each term; one measures the discriminant capability and the latter measures the characteristic capability. For each term, the former is expected to be high in accordance with the ability to distinguish a category against others, whereas the latter is expected to be high according to how the term is frequent and common over all c ategories. A preliminary study and experiments have been performed, pointing out our insight. Results confirm that, for each domain, the metrics easily identify specific stoplist wich include classical and category-dependent stopwords. (More)

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Paper citation in several formats:
Armano, G.; Fanni, F. and Giuliani, A. (2015). Stopwords Identification by Means of Characteristic and Discriminant Analysis.In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-074-1, pages 353-360. DOI: 10.5220/0005194303530360

@conference{icaart15,
author={Giuliano Armano. and Francesca Fanni. and Alessandro Giuliani.},
title={Stopwords Identification by Means of Characteristic and Discriminant Analysis},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2015},
pages={353-360},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005194303530360},
isbn={978-989-758-074-1},
}

TY - CONF

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Stopwords Identification by Means of Characteristic and Discriminant Analysis
SN - 978-989-758-074-1
AU - Armano, G.
AU - Fanni, F.
AU - Giuliani, A.
PY - 2015
SP - 353
EP - 360
DO - 10.5220/0005194303530360

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