2016 Natural Language Processing

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Academic unit or major
Graduate major in Artificial Intelligence
Tokunaga Takenobu  Fujii Atsushi 
Class Format
Media-enhanced courses
Day/Period(Room No.)
Tue1-2(W832)  Fri1-2(W832)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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 human from other animals. The aim of this course is 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;
(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

Each class starts with a discussion on the assignment of the previous class, including presentation by students on their solution, followed by a lecture on several specialised topics on the class. Students will have assignments after each class.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction: A brief history of NLP, fundamental linguistic background → Specified in the class Specified in the class.
Class 2 Morphological analysis (1): morphology, computational morphology, stemming
Class 3 Morphological analysis (2): morphological analysis, POS tagset, POS tagging
Class 4 Morphological analysis (3): rule-based morphological analysis, statistical morphological analysis
Class 5 Syntactic analysis (1): syntax, generative grammars, unification grammars
Class 6 Syntactic analysis (2): algorithms for syntactic parsing, top-down parsing, bottom-up parsing
Class 7 Syntactic analysis (3): reachability, chart parsing
Class 8 Semantic analysis (1):semantics, first order logic, knowledge representation
Class 9 Semantic analysis (2): case grammar, case frame, selectional restriction, lexical semantics, representation for time and space
Class 10 Semantic analysis (3): word sense disambiguation, semantic role labelling
Class 11 Discourse analysis (1): pragmatics, speech act theory, Grician maxim, indirect speech act
Class 12 Discourse analysis (2): reference analysis, centring theory
Class 13 Discourse analysis (3): discourse structure, discourse structure analysis, rhetorical structure theory, dialogue management
Class 14 Language resources: corpora, lexicons, annotation
Class 15 Text generation: text planning, micro planning, realisation


Not specified.
Handouts will be provided through the OCW-i system.

Reference books, course materials, etc.

Pierre M. Nugues, Language Processing with Perl and Prolog, 2nd ed. Springer (2014).
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

Submitted reports of the assignments.

Related courses

  • ART.T548 : Advanced Artificial Intelligence

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

Ability of programming.



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