| Academic Year |
2025Year |
School/Graduate School |
Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program |
| Lecture Code |
WSN21801 |
Subject Classification |
Specialized Education |
| Subject Name |
自然言語処理特論 |
Subject Name
（Katakana） |
シゼンゲンゴショリトクロン |
Subject Name in
English |
Advanced Natural Language Processing |
| Instructor |
EGUCHI KOJI |
Instructor
(Katakana) |
エグチ コウジ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, Second Semester, 3Term |
| Days, Periods, and Classrooms |
(3T) Thur1-4 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Online (simultaneous interactive) |
| Lectures, exercises, discussions, and oral presentations. |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
B
:
Japanese／English |
| Course Level |
6
:
Graduate Advanced
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
Natural language processing, machine learning, deep learning, and embedding learning. |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
A lot of intelligent human activities are supported by natural languages. Techniques for handling and analyzing such natural languages are referred to as natural language processing, a core area in artificial intelligence. Recently, natural language processing has greatly been developed using machine learning, especially deep learning. This class aims to understand basic techniques in natural language processing and machine learning. |
| Class Schedule |
lesson1: Guidance
lesson2: Introduction to machine learning for natural language processing
lesson3: Neural networks (1)
lesson4: Neural networks (2)
lesson5: RNNs and LSTMs (1)
lesson6: RNNs and LSTMs (2)
lesson7: Transformers
lesson8: Fine-tuning
lesson9: Word embeddings
lesson10: Graph embeddings
lesson11: Sense embeddings
lesson12: Contextualized embeddings (1)
lesson13: Contextualized embeddings (2)
lesson14: Sentence and document embeddings
lesson15: Conclusions and discussions
Oral presentation and discussion leading will be assigned to each registered student. |
Text/Reference
Books,etc. |
Text book:
Dan Jurafsky and James H. Martin, "Speech and Language Processing", 2025.
References:
Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola: "Dive into Deep Learning", 2023.
Mohammad Taher Pilehvar and Jose Camacho-Collados: "Embeddings in Natural Language Processing", 2020. |
PC or AV used in
Class,etc. |
Handouts, Microsoft Teams |
| (More Details) |
Slides, PC |
| Learning techniques to be incorporated |
Discussions, Project Learning |
Suggestions on
Preparation and
Review |
Your assigned reading and oral presentation are important for this class. |
| Requirements |
All registered students are expected to attend the classes, especially the first day to assign each student to a book chapter. |
| Grading Method |
Your evaluation will be based on your performance on your oral presentation, discussions, and participation in the classes. |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| 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. |