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
- 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)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W18-3212.pdf