Hiroshima University Syllabus

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Japanese
Academic Year 2022Year School/Graduate School School of Informatics and Data Science
Lecture Code KA213001 Subject Classification Specialized Education
Subject Name 自然言語処理
Subject Name
(Katakana)
シゼンゲンゴショリ
Subject Name in
English
Natural Language Processing
Instructor EGUCHI KOJI
Instructor
(Katakana)
エグチ コウジ
Campus Higashi-Hiroshima Semester/Term 3rd-Year,  First Semester,  2Term
Days, Periods, and Classrooms (2T) Weds1-4:ENG 114
Lesson Style Lecture Lesson Style
(More Details)
 
This class will mainly consist of lectures. 
Credits 2.0 Class Hours/Week   Language of Instruction B : Japanese/English
Course Level 3 : Undergraduate High-Intermediate
Course Area(Area) 25 : Science and Technology
Course Area(Discipline) 02 : Information Science
Eligible Students Undergraduate Level
Keywords Natural language processing, Probabilistic language modeling, Word embedding, Text classification, Information retrieval, and Information extraction. 
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
Program
 
Criterion referenced
Evaluation
Informatics and Data Science Program
(Knowledge and Understanding)
・I1. Knowledge and ability required for collecting and processing high-dimensional data using information processing technologies based on scientific logic, while understanding the theoretical system that forms the basis of informatics.
 
Class Objectives
/Class Outline
Class Objectives:
- To develop basic skills required for handling human languages via computers.
- To learn basic techniques and theories related to natural language processing.
- To understand typical applications of natural language processing to gain practical skills on real-world problems.

Class Outline:
A lot of intelligent human activities are supported by natural languages, such as Japanese and English. Techniques for handling and analyzing such natural languages are referred to as natural language processing. This class mainly covers four stages of natural language processing: morphological analysis, syntactic analysis, semantic analysis, and context analysis. The class also covers statistical and machine learning approaches to address disambiguation in natural languages. We further overview some applications, such as text classification, information retrieval, and information extraction. 
Class Schedule lesson 1: Introduction to natural language processing
lesson 2: Basics of character codes and text processing
lesson 3: Morphological analysis
lesson 4: Probabilistic language models
lesson 5: Text classification and Naïve Bayes
lesson 6: Information retrieval
lesson 7: Web retrieval
lesson 8: Syntactic parsing (1)
lesson 9: Syntactic parsing (2)
lesson 10: Word senses
lesson 11: Basics of neural networks
lesson 12: Word embedding
lesson 13: Information extraction
lesson 14: Semantic parsing
lesson 15: Context analysis

Exercises are conducted every week. 
Text/Reference
Books,etc.
Handouts will be provided. No textbooks will be required for this class.
Reference books will be introduced during the lectures. 
PC or AV used in
Class,etc.
 
(More Details) PDF handouts and video materials will be provided. 
Learning techniques to be incorporated  
Suggestions on
Preparation and
Review
Handouts will be provided before each class. Students are expected to look through the handouts before and after the class. 
Requirements  
Grading Method The class grade will be determined by the exercises. 
Practical Experience Experienced  
Summary of Practical Experience and Class Contents based on it The professor has experiences in developing specifications for scientific information retrieval systems and library information systems. The scope of this class covers basics of natural language processing that can be applied to various fields, including but not limited to those mentioned above. 
Message  
Other   
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. 
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