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This study describes the design of a stemming algorithm for Wolaytta language.
To give a solid background for the thesis, literatures on conflation in general and stemming algorithms in particular were reviewed.
The result of the study is a prototype context sensitive iterative stemmer for Wolaytta language.
Error counting technique was employed to evaluate the performance of this stemmer.
The stemmer was trained on 3537 words (80% of the sample text) and the improved version reveals an accuracy of 90.6% on the training set.
The number of over stemmed and understemmed words on the training set were 8.6% (304 words) and 0.8% (28 words) respectively.
When the stemmer runs on the unseen sample of 884 words (20% of the sample text), it performed with an accuracy of 86.9%.
The percentage of errors recorded as understemmed and overstemmed on this unseen (test set) were 9% and 4.1%, respectively.
Moreover, a dictionary reduction of 38.92% was attained on the test set.
The major sources of errors are also reported with possible recommendations to further improve the performance of the stemmer and also for further research.
Lemma Lessa is Lecturer in Information Systems in School of Information Sciences at Addis Ababa University (AAU), Ethiopia.
He has MSc in Information Science (AAU, 2003).
His teaching interest is on the social and management aspects of the design and use of Information Systems.
Currently he is a PhD candidate in Information Systems at AAU.
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