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Unsupervised Machine Learning Approach for Word sense Disambiguation to Amharic Words

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dc.contributor.advisor Abebe Ermias (Ato)
dc.contributor.author Assemu Solomon
dc.date.accessioned 2018-11-30T11:29:24Z
dc.date.available 2018-11-30T11:29:24Z
dc.date.issued 2011-06
dc.identifier.uri http://etd.aau.edu.et/handle/123456789/14757
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher Addis Ababa University en_US
dc.subject Machine Learning en_US
dc.title Unsupervised Machine Learning Approach for Word sense Disambiguation to Amharic Words en_US
dc.type Thesis en_US

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