GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks
Mohammed A. Attia, Younes Samih, Wolfgang Maier · 2018
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 system is ranked second among those participating in the shared task achieving an FB1 average of 70.09%.