Stephanie Antetomaso


A Word-and-Paradigm Workflow for Fieldwork Annotation
Maria Copot | Sara Court | Noah Diewald | Stephanie Antetomaso | Micha Elsner
Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages

There are many challenges in morphological fieldwork annotation, it heavily relies on segmentation and feature labeling (which have both practical and theoretical drawbacks), it’s time-intensive, and the annotator needs to be linguistically trained and may still annotate things inconsistently. We propose a workflow that relies on unsupervised and active learning grounded in Word-and-Paradigm morphology (WP). Machine learning has the potential to greatly accelerate the annotation process and allow a human annotator to focus on problematic cases, while the WP approach makes for an annotation system that is word-based and relational, removing the need to make decisions about feature labeling and segmentation early in the process and allowing speakers of the language of interest to participate more actively, since linguistic training is not necessary. We present a proof-of-concept for the first step of the workflow, in a realistic fieldwork setting, annotators can process hundreds of forms per hour.


Stop the Morphological Cycle, I Want to Get Off: Modeling the Development of Fusion
Micha Elsner | Martha Johnson | Stephanie Antetomaso | Andrea Sims
Proceedings of the Society for Computation in Linguistics 2020


Joint Word Segmentation and Phonetic Category Induction
Micha Elsner | Stephanie Antetomaso | Naomi Feldman
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)