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Automatic Arabic Part-of-Speech Tagging: Deep Learning Neural LSTM Versus Word2Vec

Show simple item record Alrajhi, Khwlah ELAffendi, Mohammed A 2019-04-30T11:17:45Z 2019-04-30T11:17:45Z 2019-05-01
dc.identifier.issn 2210-142X
dc.description.abstract Part-of-speech (POS) tagging is the process of selecting an appropriate POS tag for each word in a natural language sentence. POS tagging is a vital part of most natural language processing (NLP) applications. In comparison to other languages, there is a dearth of studies on NLP applications for the Arabic language. Recently, neural networks (NNs) and deep learning technologies have shown excellent results for some English and Latin NLP applications. However, for Arabic, the practice is still in its infancy, and more work is needed to determine whether neural technologies will lead to convincing results for NLP applications. In this paper, a long short-term memory (LSTM) model has been used to investigate the effectiveness of NNs in Arabic NLP. The model has been specifically applied to identify the POS tags for Arabic words and morphemes taken from the Quranic Arabic Corpus (QAC) data set. QAC is a well-known gold standard dataset prepared by researchers from Leeds University. It is interesting to note that LSTM tagger achieved 99.72% accuracy for tagging morphemes and 99.18% for tagging words, while the Word2Vec tagger achieved 99.55% for tagging morphemes and 97.33% for tagging words. en_US
dc.language.iso en_US en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri *
dc.subject Arabic parts of speech en_US
dc.subject Deep learning en_US
dc.subject Long short-term memory (LSTM) en_US
dc.subject Neural network (NN) en_US
dc.subject Recurrent neural network (RNN) en_US
dc.subject Tag en_US
dc.subject Word embedding en_US
dc.subject Word2Vec en_US
dc.title Automatic Arabic Part-of-Speech Tagging: Deep Learning Neural LSTM Versus Word2Vec en_US
dc.type Article en_US
dc.volume 08 en_US
dc.issue 03 en_US
dc.pagestart 307 en_US
dc.pageend 315 en_US
dc.contributor.authorcountry Saudi Arabia en_US
dc.contributor.authoraffiliation EIAS Data Science & Blockchain Lab, Department of Computer Science, Prince Sultan University en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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