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.
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
Search results for Multilingual Text Categorization: (Based on Machine...
Seller Image
Multilingual Text Categorization
Published by
Noor Publishing Aug 2017, 2017
ISBN 10: 6202343052
ISBN 13: 9786202343053
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 260 pp. Englisch. Seller Inventory # 9786202343053
Buy New
US$ 71.41
US$ 26.62 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: 2 available
Seller Image
Multilingual Text Categorization
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 167895842
Buy New
US$ 58.62
US$ 56.70 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: Over 20 available
Stock Image
Multilingual Text Categorization: (Based on Machine Learning Algorithms and Ontologies)
Seller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: Brand New. 260 pages. 8.66x5.91x0.59 inches. In Stock. Seller Inventory # zk6202343052
Buy New
US$ 125.98
US$ 13.41 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available
Seller Image
Multilingual Text Categorization
Published by
Noor Publishing Aug 2017, 2017
ISBN 10: 6202343052
ISBN 13: 9786202343053
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 260 pp. Englisch. Seller Inventory # 9786202343053
Buy New
US$ 71.41
US$ 69.44 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: 1 available
Seller Image
Multilingual Text Categorization | (Based on Machine Learning Algorithms and Ontologies)
Seller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Multilingual Text Categorization | (Based on Machine Learning Algorithms and Ontologies) | Said Gadri | Taschenbuch | 260 S. | Englisch | 2017 | Noor Publishing | EAN 9786202343053 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 109727623
Buy New
US$ 61.63
US$ 81.01 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: 5 available
Seller Image
Multilingual Text Categorization : (Based on Machine Learning Algorithms and Ontologies)
Print on DemandSeller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. 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
Buy New
US$ 72.26
US$ 71.79 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: 1 available