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Georgetown University

HELP | EXIT

Detailed Course Information

 

Fall 2017
Jan 18, 2018
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LING 572 - Empirical Methods in NLP
Systems of communication that come naturally to humans are thoroughly unnatural for computers. For truly robust information technologies, we need to teach computers to unpack our language. Natural language processing (NLP) technologies facilitate semi-intelligent artificial processing of human language text. In particular, techniques for analyzing the grammar and meaning of words and sentences can be used as components within applications such as web search, question answering, and machine translation.

This course introduces fundamental NLP concepts and algorithms, emphasizing the marriage of linguistic corpus resources with statistical and machine learning methods. As such, the course combines elements of linguistics, computer science, and data science. Coursework will consist of lectures, programming assignments (in Python), and a final team project. The course is intended for students who are already comfortable with programming and have some familiarity with probability theory. Linguistics students are recommended to complete Intro to NLP (LING-362) before enrolling in this course.

3.000 Credit hours
3.000 Lecture hours
0.000 Lab hours

Levels: MN or MC Graduate, Undergraduate
Schedule Types: Lecture

Linguistics Department

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