Computational Support for Finding Word Classes: A Case Study of Abui

Abstract

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.

Building Analyses from Syntactic Inference in Local Languages: An HPSG Grammar Inference System

“…This section focuses on our approach to inferring the grammar specifications illustrated in the previous section. We take as our starting point the system of Zamaraeva et al (2019a) which integrates the morphological inference module (called MOM; Wax, 2014;Zamaraeva, 2016;Zamaraeva et al, 2017) and a module for inference of a few syntactic properties (Bender et al, 2014;Howell et al, 2017). To this integrated system we add extended inference for morphologically marked syntactic and semantic features, additional lexical classes and further syntactic properties to create , Building Analyses from Syntactic Inference in Local languages.…”

“…The AGGREGATION project (Bender et al, 2013(Bender et al, , 2014Howell et al, 2017;Zamaraeva et al, 2017Zamaraeva et al, , 2019a, describes its primary goal as providing the benefits of implemented, formal grammars to documentary linguists, without their having to invest time in develop-ing those grammars by hand. Such grammars are useful for testing linguistic hypotheses against data (Bierwisch, 1963;Müller, 1999;Bender, 2008b;Fokkens, 2014;Müller, 2015) as well as building treebanks which are useful for discovering examples of phenomena in a language (Bender et al, 2012;Letcher and Baldwin, 2013;Bouma et al, 2015).…”

Visualizing Inferred Morphotactic Systems

“…(1 Glossed examples like this one present an analysis of morphology on one particular sentence; the linguist will want to generalize to a set of hypotheses about the general morphological system in the language. For example, a question might be: Which prefixes occur with which verb stems (Zamaraeva et al, 2017) and is there any semantic coherence to the verb inflectional classes identified this way (Kratochvíl and Delpada, 2015)? In order to answer this kind of question more fully, linguists may find it helpful not only to find all possible cooccurrences of, say, prefixes and verbs, but also to visualize them as a graph.…”

“…At the same time, building language technology for morphologically complex lowresource languages requires a rule-based morphological analyzer when datasets are not large enough for ML approaches (see Garrette et al 2013;Erdmann and Habash 2018, inter alia). Our contribution is within the context of the AGGRE-GATION project, which aims to automatically infer broad typological characteristics and morphological patterns for understudied languages (Bender et al, 2013;Zamaraeva et al, 2017). We developed this visualization tool to help linguists to understand the morphological system implicit in large datasets and to refine automatically generated grammar specifications which model that morphological system.…”

“…Our system helps the linguist visualize and explore these hypotheses, facilitating both further linguistic theorizing of the dataset and the production of a more accurate implemented grammar. The grammar can be used to produce annotations for additional unglossed data, as in Zamaraeva et al 2017, because the inferred system generalizes beyond the specific combinations of morphemes observed. 1…”