Computational support for finding word classes: A case study of Abui

We present a system that automatically groups verb stems into inflection classes, performing a case study of Abui verbs. Starting from a relatively small number of fully glossed Abui sentences, we train a morphological precision grammar and use it to automatically analyze and gloss words from the unglossed portion of our corpus. Then we group stems into classes based on their cooccurrence patterns with several prefix series of interest. We compare our results to a curated collection of elicited examples and illustrate how our approach can be useful for field linguists as it can help them refine their analysis by accounting for more patterns in the data.