Graduate Education Institute · Computer Science and Engineering · Master
Course Objective
For the purpose of this course, IR will mainly mean the study of the indexing, processing, storage and querying of textual data. The aim of the course is to provide an introduction to the core principles and techniques used in IR, and to demonstrate how statistical models of language can be used to solve document indexing and retrieval problems. In addition, we will look at the issues involved in indexing the entire web and the creative solutions to this problem currently deployed by large scale online search providers.
Course Content
Boolean Retrieval: Dictionary and postings lists, boolean querying, The term vocabulary & postings lists, Skip Pointers, Phrase Queries and Positional Indexing, Scoring, term weighting & the vector space model, Dictionaries and Tolerant Retieval, Evaluation, Relevance Feedback & Query Expansion, Probabilistic IR, Language Models for IR, Link Analysis: PageRank
Required Resources
An Introduction to Information Retrieval, by Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze
Recommended Resources
UNDERSTANDING INFORMATION RETRIEVAL SYSTEMS MANAGEMENT, TYPES, AND STANDARDS, M A R C I A J . B AT E S
Explanations
Assessment will be based on class participation, paper presentations and a comprehensive final exam.
1. All students will make presentations of the articles they have read on the assigned dates.
2. Class participation does not mean attendance. In the interactive lectures, questions will be asked to the students and individual evaluation will be made based on the answers from the students.
Rules
1. Attendance: According to the Regulation, if a student does not attend 30% of the total course hours, he/she will receive an absentee grade (DZ) and fail the course.
2. Getting Help: It is important for your education that you ask your questions about the course during the class or during the meeting hours and learn the subject in a timely manner. We encourage our students to seek help by asking questions when necessary.
3. Academic honesty: The work that will be the subject of the course grade is expected to be entirely your own effort and work. In unethical cases such as plagiarism, copying, etc., you will be subject to disciplinary investigation and penal sanctions in accordance with the relevant regulation of IZU. Works found to be copied will be graded accordingly.
4. The assessments specified in this syllabus are fixed and no extra compensatory assessment will be made for grade raising.
Course Learning Outcomes
- Gain an understanding of the basic concepts and techniques in Information Retrieval.
- understand how statistical models of text can be used to solve problems in IR, with a focus on how the vector-space model and language models are implemented and applied to document retrieval problems
- understand how statistical models of text can be used for other IR applications, for example clustering and news aggregation
- appreciate the importance of data structures, such as an index, to allow efficient access to the information in large bodies of text
- understand common text compression algorithms and their role in the efficient building and storage of inverted indices
Core Area Distribution
Teaching Methods
Assessment & Evaluation
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 14 | 3 | 42 |
| Out of Class Study Period | 14 | 6 | 84 |
| Midterm | 1 | 12 | 12 |
| Quiz | 0 | 0 | 0 |
| Assignment | 3 | 5 | 15 |
| Practice | 1 | 10 | 10 |
| Final | 1 | 24 | 24 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Boolean Retrieval: Dictionary and postings lists, boolean querying | Ch 1 |
| 2 | The term vocabulary & postings lists Skip Pointers, Phrase Queries and Positional Indexing | Ch 2 |
| 3 | Scoring, term weighting & the vector space model | Lecture Notes |
| 4 | Dictionaries and Tolerant Retieval | Lecture Notes |
| 5 | Index Constructions | Lecture Notes |
| 6 | Index Compression | Lecture Notes |
| 7 | Midterm | Lecture Notes |
| 8 | Relevance Feedback & Query Expansion | Lecture Notes |
| 9 | Probabilistic IR | Lecture Notes |
| 10 | Language Models for IR | Lecture Notes |
| 11 | Link Analysis: HITS Text Processing: Stemming, Phrases & N-grams, Link Analysis: PageRank | Lecture Notes |
| 12 | Web Crawling | Lecture Notes |
| 13 | Word2Vec (Part I and II) | Lecture Notes |
| 14 | Retrieval Models | Lecture Notes |
| 15 | Presentations | Self Study |
| 16 | Final | All lectures |