Title

Transfer Learning for Arabic Named Entity Recognition with Deep Neural Networks

DOI

10.1109/ACCESS.2020.2973319

Document Type

Journal Article

Publication Date

1-1-2020

Publication Title

IEEE Access

Volume

8

First Page

37736

Last Page

37745

Keywords

ANER, Bi-LSTM, deep learning, Natural language processing, transfer learning, universal sentence encoder

Abstract

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.

Open Access

Gold

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