Abstract
Many applications that we use on a daily basis incorporate Natural Language Processing (NLP), from simple tasks such as automatic text correction to speech recognition. A lot of research has been done on NLP for the English language but not much attention was given to the NLP of the Arabic language. The purpose of this work is to implement a tagging model for Arabic Name Entity Recognition which is an important information extraction task in NLP. It serves as a building block for more advanced tasks. We developed a deep learning model that consists of Bidirectional Long Short Term Memory and Conditional Random Field with the addition of different network layers such as Word Embedding, Convolutional Neural Network, and Character Embedding. Hyperparameters have been tuned to maximize the F1-score.
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Awad, D., Sabty, C., Elmahdy, M., Abdennadher, S. (2018). Arabic Name Entity Recognition Using Deep Learning. In: Dutoit, T., MartÃn-Vide, C., Pironkov, G. (eds) Statistical Language and Speech Processing. SLSP 2018. Lecture Notes in Computer Science(), vol 11171. Springer, Cham. https://doi.org/10.1007/978-3-030-00810-9_10
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DOI: https://doi.org/10.1007/978-3-030-00810-9_10
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