Using Stem-Templates to Improve Arabic POS and Gender/Number Tagging
By Kareem Darwish, Ahmed Abdelali, Hamdy Mubarak. 2014
36 citations recorded by the source corpus.
This paper presents an end-to-end automatic processing system for Arabic. The system performs: correction of common spelling errors pertaining to different forms of alef, ta marbouta and ha, and alef maqsoura and ya; context sensitive word segmentation into underlying clitics, POS tagging, and gender and number tagging of nouns and adjectives. We introduce the use of stem templates as a feature to improve POS tagging by 0.5\% and to help ascertain the gender and number of nouns and adjectives. For gender and number tagging, we report accuracies that are significantly higher on previously unseen words compared to a state-of-the-art system.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Natural Language Processing, Computational Linguistics & Semantics
How researchers see this paper
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In Artificial Intelligence
- Nadir Durrani
- Ahmed Abdelali
- Hamdy Mubarak
- Kareem Darwish
- Yonatan Belinkov
- Hassan Sajjad
- Fahim Dalvi
- James R. Glass
In Computational Theory and Mathematics
In Natural Language Processing
- Ahmed Abdelali
- Hamdy Mubarak
- Kareem Darwish
- Nadir Durrani
- Yonatan Belinkov
- Philipp Koehn
- Hassan Sajjad
- Fahim Dalvi
In Computational Linguistics & Semantics
Citations in the corpus
- Farasa: A Fast and Furious Segmenter for Arabic (2016, methodology): “To find stem templates, we used the module described in Darwish et al. (2014).”