All courses below are offered completely online via Adobe Connect. However, you also have the option of attending class on campus. These courses are also available for single-course enrollment and credit can be applied toward the Professional Master's in Computational Linguistics under certain conditions. Click here to learn how to apply.

Linguistics Courses

Introduction to Linguistic Phonetics (LING 550) (5 credits)
Introduction to the articulatory and acoustic correlates of phonological features. Issues covered include the mapping of dynamic events to static representations, phonetic evidence for phonological description, universal constraints on phonological structure and implications of psychological speech-sound categorization for phonological theory.

Prerequisites: either LING 200 or LING 400

Introduction to Syntax for Computational Linguistics (LING 566B) (3 credits)
Introduction to syntactic analysis and concepts (including part of speech types, constituent structure, the syntax-semantics interface, and phenomena such as complementation, raising, control, passive and long-distance dependencies). Emphasis will be placed on formally precise encoding of linguistic hypotheses and designing grammars so that they can be scaled up for practical applications. Through the course we will progressively build up a consistent grammar for a fragment of English. Problem sets will introduce data and phenomena from other languages.

Prerequisites: Introduction to Linguistics or Introduction to Computer Science

Computational Linguistics Courses

Note: These courses are arranged mostly according to subproblems in the field of computational linguistics. A few themes cut across these subproblems and will be accordingly addressed in each appropriate class, from the perspective of that class. Those themes include: evaluation, ambiguity resolution and the reusability of resources and techniques across languages. Most of the courses will be hands-on, with weekly lab assignments and/or implemented term projects.

Shallow Processing Techniques for Natural Language Processing (LING 570) (4 credits)
Techniques and algorithms for associating relatively surface-level structures and information with natural language corpora, including: Tokenization/word segmentation, POS tagging, morphological analysis, named entity recognition, chunk parsing and word-sense disambiguation. Linguistic resources that can be leveraged for these tasks (e.g., Treebank).

Prerequisites:
• CS 326 (Data Structures) or equivalent
• Stat 391 (Probability and Statistics for CS) or equivalent
• Formal grammars, formal languages, finite state automata
• Programming in Perl, C, C++, Java or Python
• Basic unix/linux commands (e.g., ls, cd, ln, sort, head)

Deep Processing Techniques for Natural Language Processing (LING 571) (4 credits)
This course covers algorithms for using precision grammars to associate deep or elaborated linguistic structures with naturally occurring linguistic data (parsing) and to associate natural language strings with input semantic representations (generation). It also covers associated techniques for disambiguation (parse, generated string) and transfer (for symbolic machine translation).

Prerequisites:
• CS 326 (Data Structures) or equivalent
• Stat 391 (Probability and Statistics for CS) or equivalent
• Formal grammars, formal languages, finite state automata
• Programming in Perl, C, C++, Java or Python
• Basic unix/linux commands (e.g., ls, cd, ln, sort, head)

Advanced Statistical Methods in Natural Language Processing (LING 572) (4 credits)
Statistical approaches to applications such as machine translation, automated lexical acquisition (monolingual and bilingual), information retrieval and question answering, with components including language modeling, alignment and document clustering.

Prerequisite: LING 570

Natural Language Processing Systems and Applications (LING 573) (4 credits)
This course looks at building coherent systems designed to tackle practical applications. Particular topics will vary year to year, but each class will consider some of the following: machine (aided) translation, speech interfaces, information retrieval/extraction, natural language query systems, dialogue systems, augmentative and alternative communication, computer assisted language learning, language documentation/linguistic hypothesis testing, spell/grammar checking, OCR, handwriting recognition and software localization.

Prerequisites: LING 570, LING 571, LING 572

View list of elective courses.