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Automatic Myanmar News Classification

http://hdl.handle.net/20.500.12678/0000004990
95735759-aad7-425e-9d87-7211ea1eeea4
926cc459-69cd-4c56-8ee6-410c3c3bed64
None
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proceeding_total-pages-401-408.pdf proceeding_total-pages-401-408.pdf (3328 Kb)
Publication type
Article
Upload type
Publication
Title
Title Automatic Myanmar News Classification
Language en
Publication date 2017-02-16
Authors
Nwet, Khin Thandar
Khine, Aye Hnin
Soe, Khin Mar
Description
Text classification is one of the majortasks of natural language processing andincluded in the interesting research areas oftext data mining, which is about looking forpatterns in natural language text. This paperapplies two well-known classificationalgorithms. Algorithms applied are NaïveBayes and k-Nearest Neighbors (KNN).These well-known algorithms are applied oncollected Myanmar News dataset. Datasetused consists from 1200 documentsbelongs to 4 categories. The goal of textclassification is to classify documents into acertain number of pre-defined categories.News corpus is used for training and testingpurpose of the classifier. Feature selectionalgorithm is used in the proposed system toselect the most relevant features fromtraining data. Results show that precisionand recall values using k-NN is betterthan Naïve Bayes. This research makes acomparative study between mentionedalgorithms.
Keywords
text classification, Natural Language Processing, Naive Bayes, k-Nearest Neighbors classifier
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/888
Journal articles
Fifteenth International Conference on Computer Applications(ICCA 2017)
Conference papers
Books/reports/chapters
Thesis/dissertations
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