Deep neural networks for Arabic information extraction
Smart and Sustainable Built Environment
ISSN: 2046-6099
Article publication date: 3 April 2020
Issue publication date: 11 December 2020
Abstract
Purpose
The purpose of this paper is to present a system based on deep neural networks to extract particular entities from natural language text, knowing that a massive amount of textual information is electronically available at present. Notably, a large amount of electronic text data indicates great difficulty in finding or extracting relevant information from them.
Design/methodology/approach
This study presents an original system to extract Arabic-named entities by combining a deep neural network-based part-of-speech tagger and a neural network-based named entity extractor. Firstly, the system extracts the grammatical classes of the words with high precision depending on the context of the word. This module plays the role of the disambiguation process. Then, a second module is used to extract the named entities.
Findings
Using deep neural networks in natural language processing, requires tuning many hyperparameters, which is a time-consuming process. To deal with this problem, applying statistical methods like the Taguchi method is much requested. In this study, the system is successfully applied to the Arabic-named entities recognition, where accuracy of 96.81 per cent was reported, which is better than the state-of-the-art results.
Research limitations/implications
The system is designed and trained for the Arabic language, but the architecture can be used for other languages.
Practical implications
Information extraction systems are developed for different applications, such as analysing newspaper articles and databases for commercial, political and social objectives. Information extraction systems also can be built over an information retrieval (IR) system. The IR system eliminates irrelevant documents and paragraphs.
Originality/value
The proposed system can be regarded as the first attempt to use double deep neural networks to increase the accuracy. It also can be built over an IR system. The IR system eliminates irrelevant documents and paragraphs. This process reduces the mass number of documents from which the authors wish to extract the relevant information using an information extraction system.
Keywords
Citation
Saadi, A. and Belhadef, H. (2020), "Deep neural networks for Arabic information extraction", Smart and Sustainable Built Environment, Vol. 9 No. 4, pp. 467-482. https://doi.org/10.1108/SASBE-03-2019-0031
Publisher
:Emerald Publishing Limited
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