An Experimental Comparison of Text Classification Techniques

2018 ◽  
Author(s):  
Suyash Lakhotia ◽  
Xavier Bresson
Keyword(s):  
Text Classification ◽  
Experimental Comparison ◽  
Classification Techniques

scholarly journals Allerdictor: fast allergen prediction using text classification techniques

2014 ◽  
Vol 30 (8) ◽  
pp. 1120-1128 ◽  
Author(s):  
Ha X. Dang ◽  
Christopher B. Lawrence
Keyword(s):  
Text Classification ◽  
Classification Techniques

Les méthodes de classification non supervisées appliquées aux textes : mesure de la performance des résultats de clustering de documents

2013 ◽  
Author(s):  
Pascal Cuxac ◽  
Jean-Charles Lamirel ◽  
Maha Ghribi
Keyword(s):  
Text Classification ◽  
Experimental Comparison ◽  
Alternative Approach ◽  
Bibliographic Data ◽  
F Measure

Nous présentons une approche alternative pour l'évaluation de la qualité de classifications non supervisées de textes basée sur des critères de rappel, précision et F-mesure non supervisés, exploitant les descripteurs associées aux classes. La comparaison expérimentale du comportement des critères classiques avec notre approche est effectuée sur des données bibliographiques.This paper presents an alternative approach to measuring the quality of non-supervised text classification based on the recall, precision and non-supervised F-measure criteria, using class descriptors. The experimental comparison of classical criteria behaviour to our approach is based on bibliographic data.

scholarly journals Semantic Ontology-Based Approach to Enhance Arabic Text Classification

2019 ◽  
Vol 3 (4) ◽  
pp. 53
Author(s):  
Ahmad Hawalah
Keyword(s):  
Text Classification ◽  
Arabic Language ◽  
Arabic Text ◽  
Inverse Document Frequency ◽  
Classification Techniques ◽  
Topic Discovery ◽  
Document Frequency ◽  
The Rich ◽  
Semantic Ontology ◽  
Arabic Text Classification

Text classification is a process of classifying textual contents to a set of predefined classes and categories. As enormous numbers of documents and contextual contents are introduced every day on the Internet, it becomes essential to use text classification techniques for different purposes such as enhancing search retrieval and recommendation systems. A lot of work has been done to study different aspects of English text classification techniques. However, little attention has been devoted to study Arabic text classification due to the difficulty of processing Arabic language. Consequently, in this paper, we propose an enhanced Arabic topic-discovery architecture (EATA) that can use ontology to provide an effective Arabic topic classification mechanism. We have introduced a semantic enhancement model to improve Arabic text classification and the topic discovery technique by utilizing the rich semantic information in Arabic ontology. We rely in this study on the vector space model (term frequency-inverse document frequency (TF-IDF)) as well as the cosine similarity approach to classify new Arabic textual documents.

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