Computer Science 243 - Natural Language Processing

This course is an introduction to natural language processing (NLP), the area of computer science that considers how computers can analyze and produce human language. We will examine core NLP tasks and implement various algorithms to address these tasks. In addition to algorithmically-focused work, we will also consider the features of different human languages and the creation of computational linguistics datasets. This course includes a sizable project which asks students to engage with and develop state-of-the-art NLP research.

Possible topics include: regular expressions, lexicons, supervised classification, n-gram language modeling, Hidden Markov Models, model evaluation, vector semantics, NLP ethics, semantic representations, word embeddings, neural networks, Transformer models, transfer learning, linguistic annotation, and dataset creation.

Requisite: COSC-211. Fall semester. Professor Wein.

How to handle overenrollment: Priority to upper-level majors, then to other majors.

Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: Quantitative work, projects.