MorphAGram, Evaluation and Framework for Unsupervised Morphological Segmentation

Ramy Eskander, Francesca Callejas, Elizabeth Nichols, Judith Klavans, Smaranda Muresan


Abstract
Computational morphological segmentation has been an active research topic for decades as it is beneficial for many natural language processing tasks. With the high cost of manually labeling data for morphology and the increasing interest in low-resource languages, unsupervised morphological segmentation has become essential for processing a typologically diverse set of languages, whether high-resource or low-resource. In this paper, we present and release MorphAGram, a publicly available framework for unsupervised morphological segmentation that uses Adaptor Grammars (AG) and is based on the work presented by Eskander et al. (2016). We conduct an extensive quantitative and qualitative evaluation of this framework on 12 languages and show that the framework achieves state-of-the-art results across languages of different typologies (from fusional to polysynthetic and from high-resource to low-resource).
Anthology ID:
2020.lrec-1.879
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
7112–7122
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.879
DOI:
Bibkey:
Cite (ACL):
Ramy Eskander, Francesca Callejas, Elizabeth Nichols, Judith Klavans, and Smaranda Muresan. 2020. MorphAGram, Evaluation and Framework for Unsupervised Morphological Segmentation. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 7112–7122, Marseille, France. European Language Resources Association.
Cite (Informal):
MorphAGram, Evaluation and Framework for Unsupervised Morphological Segmentation (Eskander et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.879.pdf