A word-and-paradigm workflow for fieldwork annotation
| Titre | A word-and-paradigm workflow for fieldwork annotation |
| Publication Type | Article dans des actes |
| Année de la conférence | 2022 |
| Authors | Copot, Maria, Sara Court, Noah Diewald, Stephanie Antetomaso, and Micha Elsner |
| Nom de la conférence | ComputEL |
| Volume | Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages |
| Publisher | Association for Computational Linguistics |
| Conference Location | Dublin, Ireland |
| Abstract | 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. |
Fichier:
LabEx EFL
Université Paris Cité
Graduate schools
