Abstract
This work describes the Edinburgh submission to the SIGMORPHON 2021 Shared Task 2 on unsupervised morphological paradigm clustering. Given raw text input, the task was to assign each token to a cluster with other tokens from the same paradigm. We use Adaptor Grammar segmentations combined with frequency-based heuristics to predict paradigm clusters. Our system achieved the highest average F1 score across 9 test languages, placing first out of 15 submissions.
Cite
CITATION STYLE
McCurdy, K., Goldwater, S., & Lopez, A. (2021). Adaptor Grammars for Unsupervised Paradigm Clustering. In SIGMORPHON 2021 - 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, Proceedings of the Workshop (pp. 82–89). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.sigmorphon-1.9
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