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The advancement of the present day technology enables the production of huge amount of information. Retrieving useful information out of these huge collections necessitates proper organization and structuring. Automatic text classification is an inevitable solution in this regard. However, the present approach focuses on the flat classification, where each topic is treated as a separate class, which is inadequate in text classification where there are a large number of classes and a huge number of relevant features needed to distinguish between them.This book explores the use of hierarchical structure for classifying a large, heterogeneous collection of Amharic News Text. The approach utilizes the hierarchical topic structure to decompose the classification task into a set of simpler problems, one at each node in the classification tree.In such a hierarchical structure document types become more specific as we go down in the hierarchy. Hence, retrieviving relvant document set become easier and efficient. The book also addresses the imprtant concepts of support Vector Machine (SVM) as a potential approach in text classification.

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ALemu Kumilachew studied Information Technology(BScIT) at Jimma University & Information Science (MScIS) at Addis Ababa University. Currently working in Bahirdar University as Lecturer in the school of Computing and Electrical Engineering, Bahirdar, Ethiopia.

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