Posted: November 15, 2021
The Department of Linguistics at the University of Massachusetts Amherst is offering a new on-line computational linguistics course, Ling 492B, Computational linguistics: Use and meaning. The course is asynchronous, so students can watch the lectures and complete the exercises at a time of the day and week that is convenient for them. It is taking place in the UMass spring semester, beginning Jan. 25th and ending May 12th. A course description and list of prerequisites follows. It is now available on-line for non-UMass students.
To register, go to: https://www.umass.edu/uww/class-enrollment. The cost of the course, including registration fee, is $1496. It is a regular 3-credit advanced undergraduate course, and may be eligible for transfer credit, subject to the granting school’s approval.
Students interested in this program who may want to transfer credits to JHU, should read up on the JHU pre-approval transfer credit process.
If you have any questions about the course, contact Brandon Prickett, the instructor, at firstname.lastname@example.org. If you have any questions about course registration, contact the UMass Amherst UWW registration office at email@example.com.
Ling 492B Computational linguistics: Use and meaning
This course is an introduction to computational linguistics, the study of natural language from a computational perspective. Computational linguistics encompasses both applied (engineering) and theoretical (cognitive) issues, and in this course you will get a taste of both. You will learn how to write code to implement key algorithms for processing and analyzing linguistic structure in language corpora. You will learn how formal language models (grammars) can be implemented computationally and used to represent linguistic structure at various levels. You will use these formal language models to automatically analyze linguistic data, and see how these models can be trained using language corpora. A major focus of the course will be on statistical techniques, especially Bayesian inference, because this forms the foundation of much current work in computational linguistics, both theoretical and applied.
Prerequisites: some familiarity with Python (functions, lists, dictionaries, file I/O, and command line) and some background in linguistics (at least one course in e.g. syntax, phonology, or general linguistics). In addition, a course in probability or statistics is recommended.