GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks

Mohammed Attia, Younes Samih, Wolfgang Maier

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Abstract
This paper describes our system submission to the CALCS 2018 shared task on named entity recognition on code-switched data for the language variant pair of Modern Standard Arabic and Egyptian dialectal Arabic. We build a a Deep Neural Network that combines word and character-based representations in convolutional and recurrent networks with a CRF layer. The model is augmented with stacked layers of enriched information such pre-trained embeddings, Brown clusters and named entity gazetteers. Our system is ranked second among those participating in the shared task achieving an FB1 average of 70.09%.
Anthology ID:
W18-3212
Volume:
Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Gustavo Aguilar, Fahad AlGhamdi, Victor Soto, Thamar Solorio, Mona Diab, Julia Hirschberg
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
98–102
Language:
URL:
https://aclanthology.org/W18-3212
DOI:
10.18653/v1/W18-3212
Bibkey:
Cite (ACL):
Mohammed Attia, Younes Samih, and Wolfgang Maier. 2018. GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 98–102, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks (Attia et al., ACL 2018)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W18-3212.pdf
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