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- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Tokunaga Takenobu
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue1-2() Fri1-2()
- Group
- -
- Course number
- ART.T459
- Credits
- 2
- Academic year
- 2021
- Offered quarter
- 3Q
- Syllabus updated
- 2021/3/19
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
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.
Student learning outcomes
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 that will be developed
| ✔ 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/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | An overview of language processing | Specified in the class. |
| Class 2 | Corpus processing tool | |
| Class 3 | Counting words | |
| Class 4 | Words, parts of speech, and morphology | |
| Class 5 | Part-of-speech tagging | |
| Class 6 | Phrase-structure grammars | |
| Class 7 | Syntactic formalism | |
| Class 8 | Phrase-structure parsing | |
| Class 9 | Dependency parsing | |
| Class 10 | Semantics and predicate logic | |
| Class 11 | Lexical semantics | |
| Class 12 | Discourse analysis | |
| Class 13 | Dialogue |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
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.
Jurafsky, D. & Martine, J. H.: Speech and Language Processing (2nd ed.), Prentice Hall (2009).
Allen, J.: Natural Language Processing 2nd ed., Benjamin (1994).
Assessment criteria and methods
Contribution to the class discussion (10%)
Quiz (30%)
Final exam (60%)
Depending on the COVID-19 epidemic situation, we might not administer the final exam. In that case, the weight would be Quiz 60% and Presentation 40%.
Related courses
- ART.T548 ： Advanced Artificial Intelligence
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Programming ability
Other
None.
