CSE 6331 Natural Language Processing
Course Summary
Natural language processing (NLP) is the ability of a computer program to understand and further generate (mostly) human language as it is spoken and written -- referred to as natural language. It is a key component of artificial intelligence (AI), and is considered a grand challenge in AI. NLP has existed for more than 50 years and has roots in the field of linguistics. This course introduces the both classical and contempory concepts in NLP especially from a statistical and machine learning approach. It aims to provide the students with a basic understanding and appreciation of key NLP theories such as lexicons, grammar, parsing and language modeling, as well as emerging NLP applications including text classification, information retrieval, machine translation, text summarization, question answering and dialogue systems. Students will practice the knowledge acquired in this course through a team project which aims at solving one particular NLP problem of their choice.
Latest News and Announcements
- 10/17/2026: The website opens.
Administrative Information
Lectures: Mon/Wed 9:25am - 12:10pm, B111 (Mon), B516, B306(Wed).
Instructor: Kenny Zhu
- ERB-535 Phone: 3420-4592 Email: kenny[dot]zhu@uta[dot]edu
Office hours: Appointments by email.
Teaching Assistant:
Zubair Faruqui
Email: zxf4122[at]mavs[dot]uta[dot]edu
Office hours: Appointments by email.
Reference Textbooks:
- Speech and Language Processing (3rd ed) by Dan Jurafsky and James Martin, The Prentice Hall.
- Foundations of Statistical Natural Language Processing by Chritopher Manning and Hinrich Schutze, The MIT Press.
- Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze, The Cambridge University Press.
Assessment:
- In-class quizzes: 20%
- Assignments: 30%
- Final Exam: 50%
Solving assignments without AI is strongly encouraged with 50% bonus.
Schedule
| Week | Date | Topic |
Slides | Resources | Homework |
Copyright (c) Kenny Q. Zhu, 2023-2026.