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Simple Search
Hits 1 – 15 of 15
1
Pre-Training BERT on Arabic Tweets: Practical Considerations ...
Abdelali, Ahmed
;
Hassan, Sabit
;
Mubarak, Hamdy
. - : arXiv, 2021
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2
Cross-lingual Emotion Detection ...
Hassan, Sabit
;
Shaar, Shaden
;
Darwish, Kareem
. - : arXiv, 2021
BASE
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3
Arabic Offensive Language on Twitter: Analysis and Experiments ...
Mubarak, Hamdy
;
Rashed, Ammar
;
Darwish, Kareem
. - : arXiv, 2020
BASE
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4
Arabic Dialect Identification in the Wild ...
Abdelali, Ahmed
;
Mubarak, Hamdy
;
Samih, Younes
. - : arXiv, 2020
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5
Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey ...
Rashed, Ammar
;
Kutlu, Mucahid
;
Darwish, Kareem
. - : arXiv, 2020
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6
A Panoramic Survey of Natural Language Processing in the Arab World ...
Darwish, Kareem
;
Habash, Nizar
;
Abbas, Mourad
. - : arXiv, 2020
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7
Arabic Curriculum Analysis ...
The 28th International Conference on Computational Linguistics 2020
;
Abdelali, Ahmed
;
Darwish, Kareem
. - : Underline Science Inc., 2020
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8
Arabic Diacritic Recovery Using a Feature-Rich biLSTM Model ...
Darwish, Kareem
;
Abdelali, Ahmed
;
Mubarak, Hamdy
;
Eldesouki, Mohamed
. - : arXiv, 2020
Abstract:
Diacritics (short vowels) are typically omitted when writing Arabic text, and readers have to reintroduce them to correctly pronounce words. There are two types of Arabic diacritics: the first are core-word diacritics (CW), which specify the lexical selection, and the second are case endings (CE), which typically appear at the end of the word stem and generally specify their syntactic roles. Recovering CEs is relatively harder than recovering core-word diacritics due to inter-word dependencies, which are often distant. In this paper, we use a feature-rich recurrent neural network model that uses a variety of linguistic and surface-level features to recover both core word diacritics and case endings. Our model surpasses all previous state-of-the-art systems with a CW error rate (CWER) of 2.86\% and a CE error rate (CEER) of 3.7% for Modern Standard Arabic (MSA) and CWER of 2.2% and CEER of 2.5% for Classical Arabic (CA). When combining diacritized word cores with case endings, the resultant word error rate is ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
;
Machine Learning cs.LG
URL:
https://dx.doi.org/10.48550/arxiv.2002.01207
https://arxiv.org/abs/2002.01207
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9
Diacritization of Maghrebi Arabic Sub-Dialects ...
Abdelali, Ahmed
;
Attia, Mohammed
;
Samih, Younes
. - : arXiv, 2018
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10
Identifying effective translations for cross-lingual Arabic-to-English user-generated speech search
Khwileh, Ahmad
;
Way, Andy
;
Jones, Gareth J.F.
...
In: Khwileh, Ahmad, Afli, Haithem orcid:0000-0002-7449-4707 , Jones, Gareth J.F. orcid:0000-0003-2923-8365 and Way, Andy orcid:0000-0001-5736-5930 (2017) Identifying effective translations for cross-lingual Arabic-to-English user-generated speech search. In: Third Arabic Natural Language Processing Workshop (WANLP), 3 Apr 2017, Valencia, Spain. (2017)
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11
Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM ...
Eldesouki, Mohamed
;
Samih, Younes
;
Abdelali, Ahmed
. - : arXiv, 2017
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12
Randomized greedy inference for joint segmentation, POS tagging and dependency parsing
Zhang, Yuan
;
Li, Chengtao
;
Barzilay, Regina
...
In: MIT Web Domain (2015)
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13
Arabizi Detection and Conversion to Arabic ...
Darwish, Kareem
. - : arXiv, 2013
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14
CLIR Experiments at Maryland for TREC-2002: Evidence Combination for Arabic-English Retrieval
Darwish, Kareem
;
Oard, Douglas W.
In: DTIC (2003)
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15
CLIR Experiments at Maryland for TREC-2002: Evidence Combination for Arabic-English Retrieval
Darwish, Kareem
;
Oard, Douglas W.
In: DTIC (2002)
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