Development of amharic morphological analyzer using memory-based learning

Abstract

Morphological analysis of highly inflected languages like Amharic is a non-trivial task because of the complexity of the morphology. In this paper, we propose a supervised data-driven experimental approach to develop Amharic morphological analyzer. We use a memory-based supervised machine learning method which extrapolates new unseen classes based on previous examples in memory. We treat morphological analysis as a classification task which retrieves the grammatical functions and properties of morphologically inflected words. As the task is geared towards analyzing the vowelled inflected Amharic words with their grammatical functions of morphemes, the morphological structure of words and the way how they are represented in memory-based learning is exhaustively investigated. The performance of the model is evaluated using 10-fold cross-validation with IB1 and IGtree algorithms resulting in the over all accuracy of 93.6% and 82.3%, respectively.