Spring 2025
LING 4400: Introduction to Natural Language Processing [ Syllabus • GU360 Site ]
Course Description: This course will introduce students to the basics of Natural Language Processing (NLP), a field that combines linguistics and computer science to produce applications, such as generative AI, that are profoundly impacting our society. We will cover a range of topics that form the basis of these exciting technological advances and will provide students with a platform for future study and research in this area. We will learn to implement simple representations such as finite-state techniques, n-gram models and basic parsing in the Python programming language. Previous knowledge of Python is not required, but students should be prepared to invest the necessary time and effort to become proficient over the course of the semester. Students who take this course will gain a thorough understanding of the fundamental methods used in natural language processing, along with an ability to assess the strengths and weaknesses of natural language technologies based on these methods.
Fall 2024
LING 4400: Introduction to Natural Language Processing [ Syllabus • GU360 Site ]
Course Description: See above
LING 8430: Information, Structure, and Language [ Syllabus • GU360 Site ]
Course Description: This seminar brings together two divergent perspectives on human language. On one hand, linguistics research seeks to describe the structures that underlie human communication systems, often using formal tools such as grammars and logics. On the other hand, research in computer science, in particular information theory, seeks to discover the optimal way to package and transmit information over a channel. This seminar will focus on the intersection between these two programs: To what extent are human languages optimized for efficient communication? Can structural features of human language, or human linguistic behaviors be analyzed using the toolkit developed for efficient information exchange? Topics covered will include the structure of the lexicon, the relationship between syntactic and statistical dependencies, pragmatic inferences, as well as various language processing phenomena. Students will gain experience reading and presenting research papers in this area, and implementing concepts from information theory in code. Students should be proficient in at least one programming language (Python or R), and familiar with basic concepts of probability theory and/or machine learning.
Experience as a TA
• Quantitative Methods in Linguistics (Harvard, Spring 2020)
• Computational Psycholinguistics (MIT, Spring 2020)
• Introduction to Linguistics (Harvard, Fall 2020)