Program: Minor in Computational Linguistics
Program Description
The minor in Computational Linguistics provides an understanding of the structure, function, and use of language, while gaining a valuable technological skillset that opens up career opportunities related to language technology.
The minor requires seven courses that give a basic foundation of linguistic theory, linguistic tools and methods, data science, and programming languages used for text and speech processing and analysis.
Program Requirements
Some courses have additional prerequisites beyond the required courses shown. Please refer to the course descriptions for details. Students are strongly encouraged to meet with the undergraduate student advisor to ensure that all prerequisites and requirements are met for the minor.
1. Required Courses (15-16 units)
COMP 110/L Introduction to Algorithms and Programming and Lab (3/1)
or COMP 102/L Programming for Data Applications and Lab (2/1)
COMP 310 Automata, Languages, and Computation (3)
or LING 455 Computational Linguistics (3)*
LING 300 Introduction to Linguistic Analysis (3)
2. Electives (6 units)
Choose two electives, one from each area.
Linguistics Area A Elective
Select one of the following:
LING 303 Human Language: Defining Our Biological Identity (3)
LING 402 Phonetics and Phonology (3)
LING 404 Syntax (3)*
LING 408 Semantics and Pragmatics (3)
Linguistics Area B Elective
Select one of the following:
LING 309 Language and Social Interaction (3)
LING 441 Sociolinguistics (3)
LING 455 Computational Linguistics (3)*
Total Units in the Minor: 21-22
*LING 404 and LING 455 cannot be used to fulfill two requirements.
Contact
Department of Linguistics/TESL
Chair: Anna Joaquin
Sierra Tower (ST) 805
(818) 677-3453
Program Learning Outcomes
Students receiving a minor in Computational Linguistics will be able to:
- Demonstrate knowledge of principles of phonetics, phonology, syntactic grammar, and semantics.
- Write programs and process texts and speech in programming languages.
- Demonstrate ability to find statistical patterns in linguistic data, identify frequency distribution, and perform tokenization.
- Extract meaning from texts.
- Apply various machine learning methods to data mining.