Classifying Amharic News Text Using Self-Organizing Maps
Number of Authors: 2
2005 (English)Conference paper (Refereed)
The paper addresses using artificial neural networks for classification of Amharic news items. Amharic is the language for countrywide communication in Ethiopia and has its own writing system containing extensive systematic redundancy. It is quite dialectally diversified and probably representative of the languages of a continent that so far has received little attention within the language processing field. The experiments investigated document clustering around user queries using Self-Organizing Maps, an unsupervised learning neural network strategy. The best ANN model showed a precision of 60.0% when trying to cluster unseen data, and a 69.5% precision when trying to classify it.
Place, publisher, year, edition, pages
2005, 1. , 8 p.
Text classification, Document Clustering, Amharic, Artificial Neural Networks, Self-Organizing Maps
Computer and Information Science
IdentifiersURN: urn:nbn:se:ri:diva-21057OAI: oai:DiVA.org:ri-21057DiVA: diva2:1041091
ACL 2005: 43rd Annual Meeting of the Association for Computational Linguistics; Workshop on Computational Approaches to Semitic Languages