Arabic Dialect Identification

By Omar Zaidan, Chris Callison-Burch. International Conference on Computational Logic, 2014

333 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/860d2a5ea3ec8c12e7576dc06f52329c59800896

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Franciska de Jong
2. Dong Nguyen
3. A. Seza Doğruöz
4. Carolyn Penstein Rosé

Citations in the corpus

• Computational Sociolinguistics: A Survey (2016, methodology): “…mixedlanguage texts, more fine-grained approaches have been explored, ranging from language identification at the sentence (Elfardy and Diab 2013; Zaidan and Callison-Burch 2013; Zampieri et al. 2014) and word level (Elfardy and Diab 2012b; King and Abney 2013; Nguyen and Doğruöz 2013; Solorio…”
• Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification (2018, background): “At the regional level, we noted limited efforts focused on dialect identification such as in (Habash et al., 2008; Elfardy & Diab, 2013; Zaidan & Callison-Burch, 2013).”