A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks
2018
Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
This paper describes our system submission to the CALCS 2018 shared task on named entity recognition on codeswitched 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
doi:10.18653/v1/w18-3212
dblp:conf/acl-codeswitch/AttiaSM18
fatcat:dbsp3xfxejcs3hy6k7m3q2ppmu