2022 Natural Language Processing

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Academic unit or major
Graduate major in Artificial Intelligence
Tokunaga Takenobu 
Class Format
Lecture    (HyFlex)
Media-enhanced courses
Day/Period(Room No.)
Tue1-2(W833)  Fri1-2(W833)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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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.


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
Class 3 Machine learning
Class 4 Language models
Class 5 Morphology and Embeddings
Class 6 Part-of-speech tagging
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.


Jurafsky, D. & Martine, J. H.: Speech and Language Processing (3rd ed.), Prentice Hall (2022?). (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



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