2019 Information Organization and Retrieval

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Academic unit or major
Graduate major in Artificial Intelligence
Instructor(s)
Fujii Atsushi 
Course component(s)
Lecture
Day/Period(Room No.)
Mon5-6(W611)  Thr5-6(W611)  
Group
-
Course number
ART.T464
Credits
2
Academic year
2019
Offered quarter
4Q
Syllabus updated
2019/3/18
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

This course provides fundamental knowledge and applied technology 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 technologies supporting information retrieval systems and methods for evaluating them. The related technology part consists of information filtering, document categorization, clustering, Web mining, and recommender systems.

This course aims to teach knowledge and skills to see through inside information retrieval systems, using related technologies 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) interaction between a user and computer in information retrieval.
(b) architectures for information retrieval systems and technology for each component.
(c) experiments, data sets, and interpretation and presentation of results related to the evaluation for information retrieval.
(d) technologies related to information retrieval and organization

Keywords

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

Competencies that will be developed

Intercultural skills Communication skills Specialist 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 Technologies related to information retrieval Explain technologies 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 recommender systems.

Textbook(s)

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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