Transfer Learning for Arabic Named Entity Recognition with Deep Neural Networks
ANER, Bi-LSTM, deep learning, Natural language processing, transfer learning, universal sentence encoder
The vast amount of unstructured data spread on a daily basis rises the need for developing effective information retrieval and extraction methods. Named Entity Recognition is a challenging classification task for structuring data into pre-defined labels, and is even more complicated when being applied on the Arabic language due to its special traits and complex nature. This article presents a novel Deep Learning approach for Standard Arabic Named Entity Recognition that proved its out-performance when being compared to previous works. The main aim of building a new model is to provide better fine-grained results for use in the Natural Language Processing fields. In our proposed methodology we utilized transfer learning with deep neural networks to build a Pooled-GRU model combined with the Multilingual Universal Sentence Encoder. Our proposed model scored about 17% enhancement when being compared to previous work.
Al-Smadi, M., Al-Zboon, S., Jararweh, Y., & Juola, P. (2020). Transfer Learning for Arabic Named Entity Recognition with Deep Neural Networks. IEEE Access, 8, 37736-37745. https://doi.org/10.1109/ACCESS.2020.2973319