MADA + TOKAN : A Toolkit for Arabic Tokenization , Diacritization , Morphological Disambiguation , POS Tagging , Stemming and Lemmatization
By Nizar Habash, Owen Rambow, Ryan Roth. 2009
349 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/8dccf1749b25bf21d9208237fd2e68299615e4e0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Nizar Habash
- Ahmed Abdelali
- Yonatan Belinkov
- Houda Bouamor
- Kareem Darwish
- Ahmed El Kholy
- Nadir Durrani
- Hamdy Mubarak
Citations in the corpus
- Can Automatic Post-Editing Make MT More Meaningful (2012, methodology): “The Arabic text is tokenized and lemmatized using the MADA+TOKAN system (Habash et al., 2009).”
- MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic (2014, methodology): “Abstract In this paper, we present MADAMIRA, a system for morphological analysis and disambiguation of Arabic that combines some of the best aspects of two previously commonly used systems for Arabic processing, MADA (Habash and Rambow, 2005; Habash et al., 2009; Habash et al., 2013) and AMIRA (Diab”
- Farasa: A Fast and Furious Segmenter for Arabic (2016, methodology): “…(Beesley et al., 1989; Beesley, 1996; Buckwalter, 2002; Khoja, 2001), light stemmers (Aljlayl and 1Buckwalter encoding is used exclusively in this paper Frieder, 2002; Darwish and Oard, 2007), and statistical word segmenters (Darwish, 2002; Habash et al., 2009; Diab, 2009; Darwish et al., 2014).”
- A Human Judgement Corpus and a Metric for Arabic MT Evaluation (2014, methodology): “We use the MADA package (Habash et al., 2009) to collect the stem and the morphological features of the hypothesis and reference translation.”
- Rich Morphology Generation Using Statistical Machine Translation (2012, methodology): “MADA+TOKAN (Habash and Rambow, 2005; Habash et al., 2009) is used to preprocess the Arabic text for generation and language modeling.”
- Using Stem-Templates to Improve Arabic POS and Gender/Number Tagging (2014, methodology): “Other popular systems for performing segmentation and POS tagging are MADA (Habash et al., 2009) and the Stanford POS tagger (Toutanova et al., 2003) both of which are trained on the PATB.”