Language Processing

Course Information

College	Anan College	Year	2024
Course Title	Language Processing
Course Code	1795402	Course Category	Specialized / Elective
Class Format	Lecture	Credits	Academic Credit: 2
Department	Course of Information Engineering	Student Grade	5th
Term	First Semester	Classes per Week	前期:2
Textbook and/or Teaching Materials	Introduction to Natural Language Processing by Machine Learning and Deep Learning ~Practical Programming Using scikit-learn and TensorFlow~ (Mitsuki Nakayama, Mynavi Publishing)
Instructor	Ota Kengo

Course Objectives

1. to be able to explain the concept of morphological analysis
2. to be able to explain the concept of parsing. 3. to be able to explain the concept of semantic analysis.
3. to be able to explain the concept of semantic analysis. 4. to be able to explain the concept of statistical language modeling.
4. to be able to explain the concept of statistical language modeling. 5. to be able to explain the concept of natural language processing by machine learning and deep learning.
5. explain the concept of natural language processing by machine learning and deep learning.

Rubric

	Ideal Level	Standard Level	Minimum Level
Achievement 1	Can perform analysis using basic algorithms of morphological analysis.	Can explain basic algorithms of morphological analysis.	Cannot explain basic algorithms of morphological analysis.
Achievement 2	Can perform analysis using basic algorithms of parsing.	Can explain basic algorithms of parsing.	Cannot explain basic algorithms of parsing.
Achievement 3	Can perform analysis using basic algorithms for semantic analysis.	Can explain basic algorithms of semantic analysis.	Cannot explain basic algorithms of semantic analysis.
Achievement 4	Understands basic algorithms of statistical language models and can implement them with reference to appropriate materials.	Can explain basic algorithms of statistical language models.	Cannot explain algorithms for statistical language models.
Achievement 5	Understands natural language processing by machine learning and deep learning, and can implement them with reference to appropriate materials.	Can explain natural language processing by machine learning and deep learning.	Cannot explain natural language processing by machine learning and deep learning.

Assigned Department Objectives

学習・教育到達度目標 D-1 （設計・システム系，情報・論理系，材料・バイオ系，力学系を含む工学の基礎となる学問分野について，自主的かつ継続的に学習できる。）

Teaching Method

Outline:

Acquire basic analysis methods of natural language processing (morphological analysis, syntactic analysis, semantic analysis, and context analysis). Students will also learn the principles of operation of applied natural language processing technologies (information retrieval, dialogue systems). In addition, students will learn natural language processing techniques based on machine learning and deep learning.

Style:

Learn the theory through lectures, and deepen understanding by conducting exercises during class as needed.

Notice:

Course Plan

			Theme	Goals
1st Semester
	1st Quarter
		1st	Introduction to Natural Language Processing	Be able to give an overview of natural language processing.
		2nd	Morphological Analysis	To be able to perform analysis using basic morphological analysis algorithms.
		3rd	Syntactic Analysis	To be able to perform analysis using basic algorithms for syntactic analysis.
		4th	Semantic and Context Analysis	To be able to perform analysis using basic semantic analysis algorithms.
		5th	Information Retrieval	Understand and explain algorithms for information retrieval.
		6th	Statistical Language Models	Understand and explain basic algorithms for statistical language modeling.
		7th	Statistical Language Models	Understand the basic algorithms of statistical language models and be able to implement them with reference to appropriate materials.
		8th	Word Variance Representation	Understand and explain the principles of word distributed representation.
	2nd Quarter
		9th	Mid-term examinations in the first semester
		10th	Natural Language Processing by Machine Learning	Understand natural language processing by machine learning, and be able to implement it with reference to appropriate materials.
		11th	Natural Language Processing by Deep Learning	Understand natural language processing by deep learning, and be able to implement it with reference to appropriate materials.
		12th	Natural Language Processing by Deep Learning	Understand natural language processing by deep learning, and be able to implement it with reference to appropriate materials.
		13th	Natural Language Processing by Deep Learning	Understand natural language processing by deep learning, and be able to implement it with reference to appropriate materials.
		14th	Dialogue system	Understand and explain the principles of dialogue systems.
		15th	GPT	Understand and explain the principles of GPT(Generative Pre-trained Transformer).
		16th	Return of answers

Evaluation Method and Weight (%)

	Examination	Quiz	Mutual Evaluations between students	Behavior	Portfolio	Other	Total
Subtotal	60	40	0	0	0	0	100
Basic Proficiency	30	20	0	0	0	0	50
Specialized Proficiency	30	20	0	0	0	0	50
Cross Area Proficiency	0	0	0	0	0	0	0