This paper presents a supervised machine learning approach to morphological analysis of Amharic verbs. We use Inductive Logic Programming (ILP), implemented in CLOG. CLOG learns rules as a first order predicate decision list. Amharic, an under-resourced African language, has very complex inflectional and derivational verb morphology, with four and five possible prefixes and suffixes respectively. While the affixes are used to show various grammatical features, this paper addresses only subject prefixes and suffixes.
The training data used to learn the morphological rules are manually prepared according to the structure of the background predicates used for the learning process. The training resulted in 108 stem extraction and 19 root template extraction rules from the examples provided. After combining the various rules generated, the program has been tested using a test set containing 1,784 Amharic verbs. An accuracy of 86.99% has been achieved, encouraging further application of the method for complex Amharic verbs and other parts of speech.
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