2018 Information Organization and Retrieval

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Academic unit or major
Graduate major in Artificial Intelligence
Fujii Atsushi 
Class Format
Media-enhanced courses
Day/Period(Room No.)
Mon5-6(W611)  Thr5-6(W611)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

This course provides fundamental knowledge and applied techniques related to information organization and retrieval, which are necessary for utilizing information, specifically textual data. The content consists of information retrieval and related technology parts. The information retrieval part consists of techniques supporting information retrieval systems and methods for evaluating those techniques. The related technique part consists of information filtering, document categorization, clustering, Web mining, and recommendation systems.

This course aims to teach knowledge and skills to see through inside information retrieval systems, using related techniques and their evaluation. This course also aims to identify the relationship between artificial intelligence research, such as natural language processing and Web mining.

Student learning outcomes

Students will be able to explain the following items.
(a) the interaction between a user and computer in information retrieval
(b) architectures for information retrieval systems and techniques for each component
(c) experiments, data sets, and interpretation and presentation of results related to the evaluation for information retrieval
(d) techniques related to information retrieval and organization


information retrieval, information organization, information needs, indexing, term weighting, retrieval models, relevance feedback, test collections, information filtering, document categorization, clustering, Web mining, recommendation systems, natural language processing, and artificial intelligence

Competencies that will be developed

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

Class flow

The material is organized as presentation slides and the following three steps are repeated on a slide-by-slide basis: 1) students take a note of the slide projected on a screen, 2) the content is explained, and 3) Q&A and optional exercises. The material is available only on a screen and thus students must take notes during the class. Every student is provided with sufficient time to finish taking notes before proceeding to the next slide.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction to information retrieval Explain information retrieval as human behavior and interactive processing with a computer system.
Class 2 Information needs Explain information needs focusing on the relation to queries.
Class 3 Indexing Explain the purpose and process of indexing.
Class 4 Extracting index terms Explain the type and extraction methods of index terms, such as characters and words.
Class 5 Term weighting Explain the motivation and effect of term weights.
Class 6 Boolean model Explain the concept, implementation, and process for the Boolean model.
Class 7 Vector space model Explain the concept, implementation, and process for the vector space model.
Class 8 Relevance feedback Explain the concept and implementation of the relevance feedback.
Class 9 Evaluation for information retrieval Explain the motivation, purpose, and method of the evaluation for information retrieval.
Class 10 Test collections for information retrieval Explain the role of test collections in the evaluation for information retrieval.
Class 11 Presentation and interpretation of experimental results Explain presentation and interpretation of experimental results by representative measures.
Class 12 Techniques related to information retrieval Explain techniques related to information retrieval, such as information filtering and document categorization.
Class 13 Web mining Explain Web mining from content, structure, and usage of the Web.
Class 14 Web retrieval model Explain the model for Web retrieval that uses Web mining
Class 15 Recommender systems Explain advantages and disadvantages of different methods for recommendation systems.


No textbook

Reference books, course materials, etc.

Manning, C. D., Raghavan, P, and Schutze, H. Introduction to Information Retrieval, Cambridge University Press, 2008.
Liu, B. Web Data Mining, Springer, 2007.

Assessment criteria and methods

written examinations: midterm (50%) and term-end (50%)

Related courses

  • ART.T459 : Natural Language Processing

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

No requirement

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