Department: PhD in Computer Science
Module Description: The objective of the course is to provide students with a broad understanding of current applications in Arabic Natural Language processing such as part-of-speech tagging, chunking, parsing, text summarization, sentiment analysis, information retrieval and extraction, machine translation etc. Students will also have hands-on experience in developing NLP systems using current tools. Students’ projects will involve both statistical and symbolic approaches to Arabic NLP.
Manning, C. & Schütze, H. (1999). Foundations of statistical natural language processing. MIT Press.
Krenn, B. & Samuelsson, C. (1997). The linguist's guide to statistics: don't panic. Los autores. http://nlp.stanford.edu/fsnlp/dontpanic.pdf
Pustejovsky, J. & Stubbs, A. (2013). Natural language annotation for machine learning. Beijing: O'Reilly.