Methodical Evaluation of Arabic Word Embeddings

Mohammed Elrazzaz, Shady Elbassuoni, Khaled Shaban, Chadi Helwe


Abstract
Many unsupervised learning techniques have been proposed to obtain meaningful representations of words from text. In this study, we evaluate these various techniques when used to generate Arabic word embeddings. We first build a benchmark for the Arabic language that can be utilized to perform intrinsic evaluation of different word embeddings. We then perform additional extrinsic evaluations of the embeddings based on two NLP tasks.
Anthology ID:
P17-2072
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
454–458
Language:
URL:
https://aclanthology.org/P17-2072
DOI:
10.18653/v1/P17-2072
Bibkey:
Cite (ACL):
Mohammed Elrazzaz, Shady Elbassuoni, Khaled Shaban, and Chadi Helwe. 2017. Methodical Evaluation of Arabic Word Embeddings. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 454–458, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Methodical Evaluation of Arabic Word Embeddings (Elrazzaz et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2072.pdf