A Word Sense Disambiguation Model for Amharic Words using Semi-Supervised Learning Paradigm
Journal Title: STAR Journal - Year 2014, Vol 3, Issue 3
Abstract
The main objective of this research was to design a WSD (word sense disambiguation) prototype model for Amharic words using semi-supervised learning method to extract training sets which minimizes the amount of the required human intervention and it can produce considerable improvement in learning accuracy. Due to the unavailability of Amharic word net, only five words were selected. These words were atena (አጠና), derese (ደረሰ), tenesa (ተáŠáˆ³), bela (በላ) and ale (አለ). A separate data sets using five ambiguous words were prepared for the development of this Amharic WSD prototype. The final classification task was done on fully labelled training set using Adaboost, bagging, and AD tree classification algorithms on WEKA package.
Authors and Affiliations
Getahun Wassie| College of Engineering and Technology, Wollega University, Post Box No: 395, Nekemte, Ethiopia, Ramesh Babu P| College of Engineering and Technology, Wollega University, Post Box No: 395, Nekemte, Ethiopia, Solomon Teferra| School of Information Science, Addis Ababa University, Post Box No: 1176, Addis Ababa, Ethiopia, Million Meshesha| School of Information Science, Addis Ababa University, Post Box No: 1176, Addis Ababa, Ethiopia
Keywords
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