New York University 4 credits Data Science
Natural Language Processing applies computational and linguistic knowledge to the processing of natural languages (English, Chinese, Spanish, Japanese). Applications include: machine translation, information extraction, information retrieval, and others. On the one hand, the class will include the modeling and representation of linguistic phenomena. On the other, it will cover methods for applying this knowledge using both manual rules and machine learning. Sample topics include: formal languages, hidden Markov models, part of speech tagging, vector-based methods, shallow and full parsing, semantic role labeling, information extraction and machine translation. Students will complete programming assignments (POS-tagging, Information Extraction, etc.) and group final projects. Note: CSCI-UA 469 (sponsor) and DS-UA 302 (non-sponsor) are cross-listed. Prerequisites: CSCI-UA 201 with a grade of C or higher.
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