2021 Information Organization and Retrieval

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Academic unit or major
Graduate major in Artificial Intelligence
Kato Makoto  Miyazaki Jun 
Course component(s)
Lecture    (ZOOM)
Day/Period(Room No.)
Tue7-8()  Fri7-8()  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
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Access Index

Course description and aims

In this lecture, we will discuss information organization and retrieval as a way to easily find information about a particular subject. In the first half of the lecture, I introduce typical classification methods and explain the basics of classification and machine learning methods for classification. In addition, I explain the knowledge and techniques necessary for organizing information, such as identifiers and record identification. In the latter half of the lecture, I explain information retrieval techniques in detail. Basic retrieval models and evaluation methods are explained, followed by lectures and discussions on the latest techniques used in modern retrieval systems, such as learning to rank and online evaluation. We will see how these technologies work with Python and other tools.

Student learning outcomes

The goal is to understand the basic ideas and concepts of information organization and retrieval, and to be able to put these into practice through exercises.


Information organization, information retrieval, classification, machine learning, metadata

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

This course will be taught with slides and programming exercises.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Fundamentals of Classification Learn the definition of classification and classification systems.
Class 2 Major Classification Methods Learn major classification methods (e.g., library classification).
Class 3 Document Classification (1) Learn how to classify documents by using machine learning approaches.
Class 4 Document Classification (2) Learn how to classify documents by using machine learning approaches.
Class 5 Document Classification Exercises Exercises on document classification using Python.
Class 6 Identifiers and Identification (1) Learn the types and characteristics of identifiers.
Class 7 Identifiers and Identification (2) Learn the techniques of record identification.
Class 8 Basics of Information Retrieval (1) Learn inverted index and basic retrieval models such as Boolean model.
Class 9 Basics of Information Retrieval (2) Learn basic retrieval models such as vector space model and probability model.
Class 10 Evaluation of Information Retrieval Learn how to build test collections and evaluation metrics.
Class 11 Information Retrieval Exercises Practice information retrieval using Elasticsearch.
Class 12 Learning to Rank (1) Learn how to perform ranking by machine learning.
Class 13 Learning to Rank (2) Learn how to perform ranking by machine learning.
Class 14 Online Evaluation Learn how to evaluate search systems in a real service.

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.



Reference books, course materials, etc.

Handouts are provided through a Web site.

Assessment criteria and methods

Course marks are based on exercises (source codes, 50%) and assignments (report contents, 50%).

Related courses

  • ART.T459 : Natural Language Processing
  • ART.T458 : Advanced Machine Learning

Prerequisites (i.e., required knowledge, skills, courses, etc.)

No requirement



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