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Nabil Laachfoubi
Hassan 1st University
Computer Science & IT Electrical & Electronics Engineering

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Arabic named entity recognition using deep learning approach Ismail El Bazi; Nabil Laachfoubi
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (471.056 KB) | DOI: 10.11591/ijece.v9i3.pp2025-2032

Abstract

Most of the Arabic Named Entity Recognition (NER) systems depend massively on external resources and handmade feature engineering to achieve state-of-the-art results. To overcome such limitations, we proposed, in this paper, to use deep learning approach to tackle the Arabic NER task. We introduced a neural network architecture based on bidirectional Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) and experimented with various commonly used hyperparameters to assess their effect on the overall performance of our system. Our model gets two sources of information about words as input: pre-trained word embeddings and character-based representations and eliminated the need for any task-specific knowledge or feature engineering. We obtained state-of-the-art result on the standard ANERcorp corpus with an F1 score of 90.6%.
Co-Authors Ismail El Bazi