Unsupervised Machine Learning Approach for Tigrigna Word Sense Disambiguation
Meresa Mebrahtu Reda · Journals & Books Hosting (International Knowledge Sharing Platform) · 2018
All human languages have words that can mean different things in different contexts.Word sense disambiguation (WSD) is an open problem of natural language processing, which governs the process of identifying which sense of a word (i.e.meaning) is used in a sentence, when the word has multiple meanings (polysemy).We use unsupervised machine learning techniques to address the problem of automatically deciding the correct sense of an ambiguous word Tigrigna texts based on its surrounding context.And we report experiments on four selected Tigrigna ambiguous words due to lack of sufficient training data; these are መደብ read as "medeb" has three different meaning (Program, Traditional bed and Grouping), ሓለፈ read as "halefe"; has four dissimilar meanings (Pass, Promote, Boss and Pass away), ሃደመ read as "hademe"; has two different meaning (Running and Building house) and, ከበረ read as "kebere"; has two different meaning (Respecting and Expensive).Finally 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 3.8.1 package."Use training set" evaluation mode was selected to learn the selected algorithms in the preprocessed dataset.We have evaluated the algorithms for the four ambiguous words and achieved the best accuracy within the range of 67 to 83.3 for EM which is encouraging result.