Spring 2021 Course Descriptions
An enormous amount of text (news articles, weblog, tweets) is created every day. Natural language processing transforms text into presumably useful data structures, enabling many applications such as real-time event tracking and question answering. In this course, we will study the mathematics and algorithms in NLP to better understand how they do what they do. We will cover a wide range of text analysis methods, include word level (topic and sentiment analysis), syntactical (grammars and parsing), semantic (meanings of words and phrases), and discourse (pronoun resolution and text structure). We will cover both rule-base systems and statistical models. We will code several algorithms applying what we learn in hands-on projects. We will come away with a deeper understanding of how text is processed by a computer.
Prerequisite: Completion of COMP 15, COMP 61, linear algebra (MATH 70, MATH 72, or equivalent), and statistics (ES 56, EE 24, or equivalent); or consent of instructor. Completion of COMP 135, COMP 136, or COMP 131 recommended but not required.