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9783659216701: Automatic Categorization Of Amharic News Text: A Machine Learning Approach

Synopsis

Currently news items subject classification in Ethiopia is done manually by journalists which is time consuming task (although they are using computer system to store and dispatch information). This research experimented the application of machine learning techniques to automatic categorization of Amharic news items. Machine learning techniques, Naïve Bayes and k Nearest Neighbor classifiers, were used to categorize the Amharic news items. 11, 024 news articles were used to do this research. To come up with good results text preparation and per-processing was done. Stop-word and words that occur in 3 or less documents were removed from the collection. Thirty-three percent of the data was used for testing purposes. The result of this research indicated that such classifiers are applicable to automatically classify Amharic news items. However, the classifiers work well when the categories contain almost evenly distributed news items. The best result obtained is by the naïve Bayes. The result of this research is promising. Nevertheless, additional works are recommended in order to come up with good result.

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Présentation de l'éditeur

Currently news items subject classification in Ethiopia is done manually by journalists which is time consuming task (although they are using computer system to store and dispatch information). This research experimented the application of machine learning techniques to automatic categorization of Amharic news items. Machine learning techniques, Naïve Bayes and k Nearest Neighbor classifiers, were used to categorize the Amharic news items. 11, 024 news articles were used to do this research. To come up with good results text preparation and per-processing was done. Stop-word and words that occur in 3 or less documents were removed from the collection. Thirty-three percent of the data was used for testing purposes. The result of this research indicated that such classifiers are applicable to automatically classify Amharic news items. However, the classifiers work well when the categories contain almost evenly distributed news items. The best result obtained is by the naïve Bayes. The result of this research is promising. Nevertheless, additional works are recommended in order to come up with good result.

Biographie de l'auteur

The author has about 12 years of experience as an IT instructor/trainer, network administrator, and IT Manager in the private, government, and public sectors which enables him study, develop & maintain a system; train/support users; communicate with IT stakeholders; lead and motivate an IT team to achieve the ever increasing demands of users.

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Surafel Teklu
ISBN 10 : 3659216704 ISBN 13 : 9783659216701
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Teklu SurafelThe author has about 12 years of experience as an IT instructor/trainer, network administrator, and IT Manager in the private, government, and public sectors which enables him study, develop & maintain a system train/su. N° de réf. du vendeur 5140449

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