TY - GEN
T1 - Part-of-speech tagging for Arabic Gulf dialect using Bi-LSTM
AU - Alharbi, Randah
AU - Magdy, Walid
AU - Darwish, Kareem
AU - AbdelAli, Ahmed
AU - Mubarak, Hamdy
N1 - Publisher Copyright:
© LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Part-of-speech (POS) tagging is one of the most important addressed areas in the natural language processing (NLP). There are effective POS taggers for many languages including Arabic. However, POS research for Arabic focused mainly on Modern Standard Arabic (MSA), while less attention was directed towards Dialect Arabic (DA). MSA is the formal variant which is mainly found in news and formal text books, while DA is the informal spoken Arabic that varies among different regions in the Arab world. DA is heavily used online due to the large spread of social media, which increased research directions towards building NLP tools for DA. Most research on DA focuses on Egyptian and Levantine, while much less attention is given to the Gulf dialect. In this paper, we present a more effective POS tagger for the Arabic Gulf dialect than currently available Arabic POS taggers. Our work includes preparing a POS tagging dataset, engineering multiple sets of features, and applying two machine learning methods, namely Support Vector Machine (SVM) classifier and bi-directional Long Short Term Memory (Bi-LSTM) for sequence modeling. We have improved POS tagging for Gulf dialect from 75% accuracy using a state-of-the-art MSA POS tagger to over 91% accuracy using a Bi-LSTM labeler.
AB - Part-of-speech (POS) tagging is one of the most important addressed areas in the natural language processing (NLP). There are effective POS taggers for many languages including Arabic. However, POS research for Arabic focused mainly on Modern Standard Arabic (MSA), while less attention was directed towards Dialect Arabic (DA). MSA is the formal variant which is mainly found in news and formal text books, while DA is the informal spoken Arabic that varies among different regions in the Arab world. DA is heavily used online due to the large spread of social media, which increased research directions towards building NLP tools for DA. Most research on DA focuses on Egyptian and Levantine, while much less attention is given to the Gulf dialect. In this paper, we present a more effective POS tagger for the Arabic Gulf dialect than currently available Arabic POS taggers. Our work includes preparing a POS tagging dataset, engineering multiple sets of features, and applying two machine learning methods, namely Support Vector Machine (SVM) classifier and bi-directional Long Short Term Memory (Bi-LSTM) for sequence modeling. We have improved POS tagging for Gulf dialect from 75% accuracy using a state-of-the-art MSA POS tagger to over 91% accuracy using a Bi-LSTM labeler.
KW - Bidirectional Long Short Term Memory (Bi-LSTM)
KW - Dialectal Arabic (DA)
KW - Gulf Arabic (GA)
KW - Part-of-Speech (POS)
UR - http://www.scopus.com/inward/record.url?scp=85059908306&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85059908306
T3 - LREC 2018 - 11th International Conference on Language Resources and Evaluation
SP - 3925
EP - 3932
BT - LREC 2018 - 11th International Conference on Language Resources and Evaluation
A2 - Isahara, Hitoshi
A2 - Maegaard, Bente
A2 - Piperidis, Stelios
A2 - Cieri, Christopher
A2 - Declerck, Thierry
A2 - Hasida, Koiti
A2 - Mazo, Helene
A2 - Choukri, Khalid
A2 - Goggi, Sara
A2 - Mariani, Joseph
A2 - Moreno, Asuncion
A2 - Calzolari, Nicoletta
A2 - Odijk, Jan
A2 - Tokunaga, Takenobu
PB - European Language Resources Association (ELRA)
T2 - 11th International Conference on Language Resources and Evaluation, LREC 2018
Y2 - 7 May 2018 through 12 May 2018
ER -