2023 Faculty Courses School of Computing Department of Mathematical and Computing Science Graduate major in Artificial Intelligence
Natural Language Processing
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Takenobu Tokunaga
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-2 Tue (W8E-307(W833)) / 1-2 Fri (W8E-307(W833))
- Class
- -
- Course Code
- ART.T459
- Number of credits
- 200
- Course offered
- 2023
- Offered quarter
- 3Q
- Syllabus updated
- Jul 8, 2025
- Language
- English
Syllabus
Course overview and goals
This course provides an introduction to the field of natural language processing (NLP), introducing fundamental concepts and techniques for processing human languages by computers. The course covers a linguistic background necessary for NLP, morphological analysis, syntactic analysis, semantic analysis, discourse analysis and text generation. The course also includes a part of corpus linguistics.
Linguistic competence is believed to be the most prominent human nature that distinguishes humans from other animals. This course aims to provide students with the ability to utilise fundamental NLP techniques to build language-related application systems, such as information extraction, question answering and dialogue systems.
Course description and aims
At the end of the course, students should be able to
(1) explain basic concepts of linguistics,
(2) explain basic concepts of natural language processing and
(3) build sample application programs based on the above concepts.
Keywords
computational linguistics, corpus linguistics, morphological analysis, syntactic analysis, semantic analysis, discourse analysis, language resources, text generation.
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Students must prepare the specified section in the textbook. Each class starts with a quiz on the specified section, followed by the discussion on the answers to the quiz and the contents of the specified section.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | An overview of language processing | Specified in the class. |
| Class 2 | Corpus processing | Specified in the class. |
| Class 3 | Machine learning | Specified in the class. |
| Class 4 | Vector semantics and embeddings | Specified in the class. |
| Class 5 | Language models | Specified in the class. |
| Class 6 | Sequential labelling and pretrained language models | Specified in the class. |
| Class 7 | Constituency grammars | Specified in the class. |
| Class 8 | Constituency parsing | Specified in the class. |
| Class 9 | Dependency parsing | Specified in the class. |
| Class 10 | Semantics and predicate logic | Specified in the class. |
| Class 11 | Semantic analysis | Specified in the class. |
| Class 12 | Discourse analysis | Specified in the class. |
| Class 13 | Dialogue | Specified in the class. |
Study advice (preparation and review)
Students must read the assigned chapters before attending each class. We administer a quiz for the chapters at the beginning of each class.
Textbook(s)
Jurafsky, D. & Martine, J. H.: Speech and Language Processing (3rd ed.), Prentice Hall (2023+). (https://web.stanford.edu/~jurafsky/slp3/)
Pierre M. Nugues, Language Processing with Perl and Prolog, 2nd ed. Springer (2014). (http://link.springer.com/content/pdf/10.1007%2F978-3-642-41464-0.pdf)
Reference books, course materials, etc.
Allen, J.: Natural Language Processing 2nd ed., Benjamin (1994).
Evaluation methods and criteria
Contribution to the class discussion (10%)
Quiz (30%)
Final exam (60%)
Related courses
- ART.T548 ： Advanced Artificial Intelligence
Prerequisites
Programming ability
Other
None.