1 |
Morphologically Annotated Corpora and Morphological Analyzers for Moroccan and Sanaani Yemeni Arabic
|
|
|
|
In: 10th Language Resources and Evaluation Conference (LREC 2016) ; https://hal.archives-ouvertes.fr/hal-01349201 ; 10th Language Resources and Evaluation Conference (LREC 2016), May 2016, Portoroz, Slovenia (2016)
|
|
BASE
|
|
Show details
|
|
2 |
A Large Scale Corpus of Gulf Arabic
|
|
|
|
In: Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-01349204 ; Language Resources and Evaluation Conference, 2016, Portoroz, Slovenia (2016)
|
|
BASE
|
|
Show details
|
|
3 |
Exploiting Arabic Diacritization for High Quality Automatic Annotation
|
|
|
|
In: Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-01349206 ; Language Resources and Evaluation Conference, 2016, Portoroz, Slovenia (2016)
|
|
BASE
|
|
Show details
|
|
4 |
DALILA: The Dialectal Arabic Linguistic Learning Assistant
|
|
|
|
In: Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-01349203 ; Language Resources and Evaluation Conference, 2016, Portoroz, Slovenia (2016)
|
|
BASE
|
|
Show details
|
|
5 |
Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015 ...
|
|
Sajjad, Hassan; Durrani, Nadir; Guzman, Francisco; Nakov, Preslav; Abdelali, Ahmed; Vogel, Stephan; Salloum, Wael; Kholy, Ahmed El; Habash, Nizar. - : arXiv, 2016
|
|
Abstract:
The paper describes the Egyptian Arabic-to-English statistical machine translation (SMT) system that the QCRI-Columbia-NYUAD (QCN) group submitted to the NIST OpenMT'2015 competition. The competition focused on informal dialectal Arabic, as used in SMS, chat, and speech. Thus, our efforts focused on processing and standardizing Arabic, e.g., using tools such as 3arrib and MADAMIRA. We further trained a phrase-based SMT system using state-of-the-art features and components such as operation sequence model, class-based language model, sparse features, neural network joint model, genre-based hierarchically-interpolated language model, unsupervised transliteration mining, phrase-table merging, and hypothesis combination. Our system ranked second on all three genres. ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://dx.doi.org/10.48550/arxiv.1606.05759 https://arxiv.org/abs/1606.05759
|
|
BASE
|
|
Hide details
|
|
|
|