Multilingual Text Categorization : (Based on Machine Learning Algorithms and Ontologies)

Said Gadri

ISBN 10: 6202343052 / ISBN 13: 9786202343053

Published by Noor Publishing, 2017

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Text categorization is an important task in text mining process that consists in assigning a set of texts to a set of predefined categories based on learning algorithms. There exist two kinds of text categorization: monolingual and multilingual text categorization. The main problematic of this manuscript is how to exploit concepts and algorithms of machine learning in contextual categorization of multilingual texts. Our study on this subject allowed us to propose many solutions and provide many contributions, notably: (1) a simple, fast and effective algorithm to identify the language of a text in multilingual corpus. (2) An improved algorithm for Arabic stemming based on a statistical approach. Its main objective is to reduce the size of term vocabulary and thus increase the quality of the obtained categorization in TC and the effectiveness of search in IR. (3) A new multilingual stemmer which is general and completely independent of any language. (4) Application of new panoply of pseudo-distances to categorize texts of a big corpus such as Reuters21578 collection. All these solutions were the subject of many academic papers published in international conferences and journals.

Seller Inventory # 9786202343053

Bibliographic Details

• Title: Multilingual Text Categorization : (Based on Machine Learning Algorithms and Ontologies)
• Publisher: Noor Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 6202343052
• ISBN 13: 9786202343053
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 16D millimeters 220 millimeters width by 150 millimeters height by 16 millimeters depth
• Weight: 405 grams

About this title

Synopsis

Text categorization is an important task in text mining process that consists in assigning a set of texts to a set of predefined categories based on learning algorithms. There exist two kinds of text categorization: monolingual and multilingual text categorization. The main problematic of this manuscript is how to exploit concepts and algorithms of machine learning in contextual categorization of multilingual texts. Our study on this subject allowed us to propose many solutions and provide many contributions, notably: (1) a simple, fast and effective algorithm to identify the language of a text in multilingual corpus. (2) An improved algorithm for Arabic stemming based on a statistical approach. Its main objective is to reduce the size of term vocabulary and thus increase the quality of the obtained categorization in TC and the effectiveness of search in IR. (3) A new multilingual stemmer which is general and completely independent of any language. (4) Application of new panoply of pseudo-distances to categorize texts of a big corpus such as Reuters21578 collection. All these solutions were the subject of many academic papers published in international conferences and journals.

About the Author

Received his degrees of: engineer in CS, 1996 Algeria, magister 2006 Algeria, doctor in 2016 Algeria. He is an associate professor of CS at the university of M’sila since 2007. He is a member of the scientific council of maths&CS faculty since 2009. Working in many areas of research. Published many papers in international journals and conferences.