  Morphological analysis and disambiguation is an important task and a crucial
preprocessing step in natural language processing of morphologically rich
languages. Kinyarwanda, a morphologically rich language, currently lacks tools
for automated morphological analysis. While linguistically curated finite state
tools can be easily developed for morphological analysis, the morphological
richness of the language allows many ambiguous analyses to be produced,
requiring effective disambiguation. In this paper, we propose learning to
morphologically disambiguate Kinyarwanda verbal forms from a new stemming
dataset collected through crowd-sourcing. Using feature engineering and a
feed-forward neural network based classifier, we achieve about 89%
non-contextualized disambiguation accuracy. Our experiments reveal that
inflectional properties of stems and morpheme association rules are the most
discriminative features for disambiguation.
