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2023　Natural Language Processing

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Academic unit or major
Graduate major in Artificial Intelligence
Instructor(s)
Tokunaga Takenobu 
Class Format
Lecture    (Face-to-face)
Media-enhanced courses
Day/Period(Room No.)
Tue1-2(W8E-307(W833))  Fri1-2(W8E-307(W833))  
Group
-
Course number
ART.T459
Credits
2
Academic year
2023
Offered quarter
3Q
Syllabus updated
2023/3/20
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 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.

Out-of-Class Study Time (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).

Assessment criteria and methods

Contribution to the class discussion (10%)
Quiz (30%)
Final exam (60%)

Related courses

  • ART.T548 ： Advanced Artificial Intelligence

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Programming ability

Other

None.

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