▶Japanese
Post to SNS
- Home
- > School of Computing
- > Graduate major in Artificial Intelligence
- > Natural Language Processing
- School of Science
-
School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Tokunaga Takenobu Fujii Atsushi
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue1-2(W832) Fri1-2(W832)
- Group
- -
- Course number
- ART.T459
- Credits
- 2
- Academic year
- 2016
- Offered quarter
- 3Q
- Syllabus updated
- 2016/4/27
- Lecture notes updated
- -
- Language used
- Japanese
- 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.
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
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 |
Textbook(s)
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.
Other
None.
