Automatic Indexing of News Videos Through Text Classification Techniques

2005 ◽  
pp. 512-521 ◽  
Author(s):  
G. Percannella ◽  
D. Sorrentino ◽  
M. Vento
Keyword(s):  
Text Classification ◽  
Automatic Indexing ◽  
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

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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