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

Mohammed Attia, Younes Samih, Wolfgang Maier


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)
Copy Citation:
PDF:
https://aclanthology.org/W18-3212.pdf