POS Tagging for Improving Code-Switching Identification in Arabic

Mohammed Attia, Younes Samih, Ali Elkahky, Hamdy Mubarak, Ahmed Abdelali, Kareem Darwish

Abstract
When speakers code-switch between their native language and a second language or language variant, they follow a syntactic pattern where words and phrases from the embedded language are inserted into the matrix language. This paper explores the possibility of utilizing this pattern in improving code-switching identification between Modern Standard Arabic (MSA) and Egyptian Arabic (EA). We try to answer the question of how strong is the POS signal in word-level code-switching identification. We build a deep learning model enriched with linguistic features (including POS tags) that outperforms the state-of-the-art results by 1.9% on the development set and 1.0% on the test set. We also show that in intra-sentential code-switching, the selection of lexical items is constrained by POS categories, where function words tend to come more often from the dialectal language while the majority of content words come from the standard language.
Anthology ID:
W19-4603
Volume:
Proceedings of the Fourth Arabic Natural Language Processing Workshop
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Wassim El-Hajj, Lamia Hadrich Belguith, Fethi Bougares, Walid Magdy, Imed Zitouni, Nadi Tomeh, Mahmoud El-Haj, Wajdi Zaghouani
Venue:
WANLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
18–29
Language:
URL:
https://aclanthology.org/W19-4603
DOI:
10.18653/v1/W19-4603
Bibkey:
Cite (ACL):
Mohammed Attia, Younes Samih, Ali Elkahky, Hamdy Mubarak, Ahmed Abdelali, and Kareem Darwish. 2019. POS Tagging for Improving Code-Switching Identification in Arabic. In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 18–29, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
POS Tagging for Improving Code-Switching Identification in Arabic (Attia et al., WANLP 2019)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/W19-4603.pdf
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