Distantly Supervised POS Tagging of Low-Resource Languages under Extreme Data Sparsity: The Case of Hittite

Maria Sukhareva, Francesco Fuscagni, Johannes Daxenberger, Susanne Görke, Doris Prechel, Iryna Gurevych


Abstract
This paper presents a statistical approach to automatic morphosyntactic annotation of Hittite transcripts. Hittite is an extinct Indo-European language using the cuneiform script. There are currently no morphosyntactic annotations available for Hittite, so we explored methods of distant supervision. The annotations were projected from parallel German translations of the Hittite texts. In order to reduce data sparsity, we applied stemming of German and Hittite texts. As there is no off-the-shelf Hittite stemmer, a stemmer for Hittite was developed for this purpose. The resulting annotation projections were used to train a POS tagger, achieving an accuracy of 69% on a test sample. To our knowledge, this is the first attempt of statistical POS tagging of a cuneiform language.
Anthology ID:
W17-2213
Volume:
Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
Month:
August
Year:
2017
Address:
Vancouver, Canada
Editors:
Beatrice Alex, Stefania Degaetano-Ortlieb, Anna Feldman, Anna Kazantseva, Nils Reiter, Stan Szpakowicz
Venue:
LaTeCH
SIG:
SIGHUM
Publisher:
Association for Computational Linguistics
Note:
Pages:
95–104
Language:
URL:
https://aclanthology.org/W17-2213
DOI:
10.18653/v1/W17-2213
Bibkey:
Cite (ACL):
Maria Sukhareva, Francesco Fuscagni, Johannes Daxenberger, Susanne Görke, Doris Prechel, and Iryna Gurevych. 2017. Distantly Supervised POS Tagging of Low-Resource Languages under Extreme Data Sparsity: The Case of Hittite. In Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 95–104, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Distantly Supervised POS Tagging of Low-Resource Languages under Extreme Data Sparsity: The Case of Hittite (Sukhareva et al., LaTeCH 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-2213.pdf