Natural Language Processing

Course title

L0036000

Middle-level Diploma Policy (mDP)

Program / Major	mDP	Goals	Courses
Fundamental Mechanical Engineering	F	産業界や社会の要請を把握して解決するべき課題を設定し、さまざまな工学分野の知識を関連付けながら設計生産技術を活用することで、立案した構想に従って研究を進め課題を解決することができる。	Sub
Advanced Mechanical Engineering	F	産業界や社会の要請を把握して解決するべき課題を設定し、機械工学の学理を応用して異分野を含む融合分野で革新的な機能を創成することができる。	Sub
Environment and Materials Engineering	B	地球環境や地域社会との調和を見据えて、さまざまな工学分野に関わる問題を解決することができる。	Sub
Chemistry and Biotechnology	B	地球環境や地域社会との調和を見据えて、さまざまな工学分野に関わる問題を解決することができる。	Sub
Electrical Engineering and Robotics	D	電気工学や関連する工学の技術分野を課題に適用し、社会の要求を解決するために応用することができる。	Sub
Advanced Electronic Engineering	E	専門的デザイン課題について解決する能力を身に付けることができる。	Sub
Information and Communications Engineering	F	社会のニーズに対して技術課題を主体的に発見し、工学分野における分野横断的な知識も活用しつつ、計画的・継続的に取り組んで課題を達成することができる。	Sub
Computer Science and Engineering	B-2	コンピュータサイエンスの各分野の基礎知識とその応用能力を身に付けることができる。	Main
Urban Infrastructure and Environment	G	⼟⽊⼯学における現実の問題について、⼯学・専⾨基礎知識を⽤いて理解・解決することができる。	Sub

Purpose of class

Students can learn basic theories and technologies to deal with natural language data.

Course description

Basic technology to deal with natural language text, in particular, analyze method of natural language sentences, information retrieval and machine translation is explained.

Goals and objectives

To understand grammatical features of Japanese and regularities for text construction
To understand basic technology, e.g., morphological and syntactic analyses
To learn application technology of information retrieval and machine translation

Relationship between 'Goals and Objectives' and 'Course Outcomes'

	Report	Total.
1.	20%	20%
2.	40%	40%
3.	40%	40%
Total.	100%	-

Evaluation method and criteria

Report (100%)
Completing each assignment by understanding its requirements, creating and executing the program, and submitting a report summarizing the results is the basis for achieving a score of 60 points.

Language

Japanese

Class schedule

	Class schedule	HW assignments (Including preparation and review of the class.)	Amount of Time Required
1.	Overview of natural language processing	Read Syllabus	90minutes
1.	Overview of natural language processing	Review	100minutes
2.	Japanese grammar and corpus	Read materials	90minutes
2.	Japanese grammar and corpus	Exercises	100minutes
3.	Machine learning	Read materials	90minutes
3.	Machine learning	Exercises	100minutes
4.	Morphological analysis (1) analysis method	Read materials	90minutes
4.	Morphological analysis (1) analysis method	Exercises	100minutes
5.	Morphological analysis (2) cost estimation	Read materials	90minutes
5.	Morphological analysis (2) cost estimation	Exercises	100minutes
6.	Syntactic analysis	Read materials	90minutes
6.	Syntactic analysis	Exercises	100minutes
7.	Semantic analysis (1) word meaning	Read materials	90minutes
7.	Semantic analysis (1) word meaning	Exercises	100minutes
8.	Semantic analysis (2) sentence meaning	Read materials	90minutes
8.	Semantic analysis (2) sentence meaning	Exercises	100minutes
9.	Application (1) information retrieval, text classification	Read materials	90minutes
9.	Application (1) information retrieval, text classification	Exercises	100minutes
10.	Application (2) machine translation, dialogue systems	Read materials	90minutes
10.	Application (2) machine translation, dialogue systems	Exercises	100minutes
11.	Deep learning (1) RNN, Transformer	Read materials	90minutes
11.	Deep learning (1) RNN, Transformer	Exercises	100minutes
12.	Deep learning (2) BERT, GPT	Read materials	90minutes
12.	Deep learning (2) BERT, GPT	Exercises	100minutes
13.	Deep learning (3) Large Language Models	Read materials	90minutes
13.	Deep learning (3) Large Language Models	Exercises	100minutes
14.	Summary and future directions	Review all	90minutes
14.	Summary and future directions	Exercises	100minutes
Total.	-	-	2660minutes

Feedback on exams, assignments, etc.

ways of feedback	specific contents about "Other"
Feedback in the class

Textbooks and reference materials

Reference materials are:
T. Sugimoto and S. Iwashita, ”Natural Language Processing and Machine Learning in Java”, Ohm-sha, 2018
C. Okazaki et. al., ”Basics of Natural Language Processing”, Ohm-sha, 2022
S. Kurohashi, ”Natural Language Processing, 3rd edition”, Open University of Japan, 2023

Prerequisites

Knowledge about Python language, Data Structures and Algorithms, and Artificial Intelligence.

Office hours and How to contact professors for questions

Thursday, lunch break

Regionally-oriented

Non-regionally-oriented course

Development of social and professional independence

Course that cultivates an ability for utilizing knowledge
Course that cultivates a basic problem-solving skills

Active-learning course

More than one class is interactive

Course by professor with work experience

Work experience	Work experience and relevance to the course content if applicable
N/A	N/A

Education related SDGs:the Sustainable Development Goals

4.QUALITY EDUCATION
9.INDUSTRY, INNOVATION AND INFRASTRUCTURE

Last modified : Sat Mar 14 14:23:49 JST 2026