Investigating Machine Learning Methods for Language and Dialect Identification of Cuneiform Texts

Ehsan Doostmohammadi, Minoo Nassajian


Abstract
Identification of the languages written using cuneiform symbols is a difficult task due to the lack of resources and the problem of tokenization. The Cuneiform Language Identification task in VarDial 2019 addresses the problem of identifying seven languages and dialects written in cuneiform; Sumerian and six dialects of Akkadian language: Old Babylonian, Middle Babylonian Peripheral, Standard Babylonian, Neo-Babylonian, Late Babylonian, and Neo-Assyrian. This paper describes the approaches taken by SharifCL team to this problem in VarDial 2019. The best result belongs to an ensemble of Support Vector Machines and a naive Bayes classifier, both working on character-level features, with macro-averaged F1-score of 72.10%.
Anthology ID:
W19-1420
Volume:
Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects
Month:
June
Year:
2019
Address:
Ann Arbor, Michigan
Venue:
VarDial
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
188–193
Language:
URL:
https://aclanthology.org/W19-1420
DOI:
10.18653/v1/W19-1420
Bibkey:
Cite (ACL):
Ehsan Doostmohammadi and Minoo Nassajian. 2019. Investigating Machine Learning Methods for Language and Dialect Identification of Cuneiform Texts. In Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 188–193, Ann Arbor, Michigan. Association for Computational Linguistics.
Cite (Informal):
Investigating Machine Learning Methods for Language and Dialect Identification of Cuneiform Texts (Doostmohammadi & Nassajian, VarDial 2019)
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PDF:
https://aclanthology.org/W19-1420.pdf