We introduce Arabic Data Science Toolkit (ADST), a framework for Arabic language feature extraction, designed for data scientists that may not be familiar with Arabic or natural language processing. The functions in the toolkit allow data scientists to extend their algorithms beyond lexical or statistical methods and leverage Arabic-specific linguistic and stylistic features to enhance their systems and enable them to reach performance levels they might receive on languages with more resources, or languages with which they have more familiarity.
@InProceedings{RODRIGUES18.998, author = {Paul Rodrigues and Valerie Novak and C. Anton Rytting and Julie Yelle and Jennifer Boutz}, title = "{Arabic Data Science Toolkit: An API for Arabic Language Feature Extraction}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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