Natural Language Processing

This course provides an introduction to the field of Natural Language Processing (NLP) at an undergraduate level. We will discuss properties of human language at different levels of representation (morphology, syntax, semantics, pragmatics), and will learn how to create systems that can analyze, understand, and generate natural language. We will study machine learning methods used in NLP such as various forms of Neural networks and will focus particularly on conceptual and technical advances of frontier Large Language Models based NLP technologies (think ChatGPT) that are revolutionizing classical computational linguistics and NLP fields. We will also discuss applications such as question answering, summarization, language generation and as well as data, benchmarks and evaluation frameworks. We will discuss ethical aspects of NLP research and applications. Homework assignments will consist of programming projects in Python as well as written interpretation and analysis of the results. Class will also have a midterm and a mini final project instead of a final exam. Prerequisite(s): COMS W3134 or W3136 or W3137 (or equivalent). Background in probability/statistics and linear algebra is also required and experience with Python programming is strongly encouraged. Some previous or concurrent exposure to AI and machine learning is beneficial, but not required. Please note: Due to significant overlap in content, only one of COMS BC3705 or COMS W4705 may be taken for credit.