AAU-ETD AAU-ETD
 

Addis Ababa University Libraries Electronic Thesis and Dissertations: AAU-ETD! >
Faculty of Informatics >
Thesis - Information Science >

Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3525

Title: UNSUPERVISED MACHINE LEARNING APPROACH FOR WORD SENSE DISAMBIGUATION TO AMHARIC WORDS
Authors: SOLOMON, ASSEMU
Advisors: Ato Ermias Abebe
Keywords: Information science
Copyright: Jun-2011
Date Added: 30-Jul-2012
Publisher: AAU
Abstract: Word Sense Disambiguation (WSD) in text is still a difficult problem as the best supervised methods require laborious and costly manual preparation of tagged training data. This work presents a corpus based approach to word sense disambiguation that only requires information that can be automatically extracted from untagged text. We use unsupervised techniques to address the problem of automatically deciding the correct sense of an ambiguous word based on its surrounding context. It was motivated by its use in many crucial applications such as Information Retrieval (IR), Information Extraction (IE), Machine Translation (MT), etc. For this study, we report experiments on five selected Amharic ambiguous words, these are አጠና (eTena), መሳል (mesal), መሣሣት (me`sa`sat), መጥራት (metrat), and ቀረጸ (qereSe). For the purposes of this research, unsupervised machine learning technique was applied to a corpus of Amharic sentences so as to acquire disambiguation information automatically. A total of 1045 English sense examples for the five ambiguous words were collected from British National Corpus (BNC). The sense examples were translated to Amharic using the Amharic-English dictionary and preprocessed to make it ready for experimentation. We tested five clustering algorithms (simple k means, hierarchical agglomerative: Single, Average and complete link and Expectation Maximization algorithms) in the existing implementation of Weka 3.6.4 package. “Class to cluster” evaluation mode was selected to learn the selected algorithms in the preprocessed dataset. The achieved result was encouraging, because best clustering algorithms were close in terms of accuracy of supervised machine learning approaches on the same dataset, using the same features. But, further experiments for other ambiguous words and using different approaches will be needed for a better natural language understanding of Amharic language.
Description: A Thesis Submitted to the School of Graduate Studies of Addis Ababa University in Partial Fulfillment of the Requirements for the Degree of Master of Science in Information Science
URI: http://hdl.handle.net/123456789/3525
Appears in:Thesis - Information Science

Files in This Item:

File Description SizeFormat
Solomon's Assemu Disambugation Final Thesis.pdf829.4 kBAdobe PDFView/Open

Items in the AAUL Digital Library are protected by copyright, with all rights reserved, unless otherwise indicated.

 

  Last updated: May 2010. Copyright © Addis Ababa University Libraries - Feedback