Research Area: Machine Learning
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.
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Author(s) Name: Mohammad Al-Smadi; Saad Al-Zboon; Yaser Jararweh; Patrick Juola
Journal name: IEEE Access
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/ACCESS.2020.2973319
Volume Information: ( Volume: 8) Page(s): 37736 - 37745
