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Found 9 papers, 1 papers with code
Arabic Diacritic Recovery Using a Feature-Rich biLSTM Model
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).
A System for Diacritizing Four Varieties of Arabic
Short vowels, aka diacritics, are more often omitted when writing different varieties of Arabic including Modern Standard Arabic (MSA), Classical Arabic (CA), and Dialectal Arabic (DA).
QC-GO Submission for MADAR Shared Task: Arabic Fine-Grained Dialect Identification
This paper describes the QC-GO team submission to the MADAR Shared Task Subtask 1 (travel domain dialect identification) and Subtask 2 (Twitter user location identification).
Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM
Arabic word segmentation is essential for a variety of NLP applications such as machine translation and information retrieval.
Ranked #1 on
Sentiment Analysis
on DynaSent
(using extra training data)
Learning from Relatives: Unified Dialectal Arabic Segmentation
Arabic dialects do not just share a common koin{\'e}, but there are shared pan-dialectal linguistic phenomena that allow computational models for dialects to learn from each other.
A Neural Architecture for Dialectal Arabic Segmentation
The automated processing of Arabic Dialects is challenging due to the lack of spelling standards and to the scarcity of annotated data and resources in general.
Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet
However, we show that augmenting bi-LSTM sequence labeling with some of the features that we used for the SVM-Rank based tagger yields to further improvements.
QCRI @ DSL 2016: Spoken Arabic Dialect Identification Using Textual Features
We submitted four runs to the Arabic sub-task.
