massive-data-computing/IR

2024년 봄학기 정보검색 과목 홈페이지

Textbook

• Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008
  • Book Homepage: https://nlp.stanford.edu/IR-book/
• PDF of the book for online viewing (with nice hyperlink features, 2009.04.01)
  • https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf
• PDF of the book for printing (2009.04.01)
  • https://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf

Slides

• University of Munich slides and assignments (2013.09.13)
  • http://cislmu.org/

Grading

• Midterm Exam: 40%
• Final Exam: 40%
• Assignments: 10%
• Attendance: 10%

Office Hour

• 월요일 14:00 – 17:00

Schedule

WEEK 1

• Chapter 1. Boolean Retrieval
  • 외부 강의자료 링크: https://www.cis.lmu.de/~hs/teach/14s/ir/pdf/01intro.flat.pdf
  • 추가 강의자료: https://github.com/massive-data-computing/IR/blob/main/%EC%A0%95%EB%B3%B4%EA%B2%80%EC%83%89W1.pptx

WEEK 2

• Chapter 2. The term vocabulary and postings lists

WEEK 3/WEEK 4

• Chapter 3. Dictionaries and tolerant retrieval

WEEK 5

• Chapter 4. Index construction

WEEK 6

• Chapter 5. Index compression

WEEK 7

• Chapter 6. Scoring, term weighting and the vector space model

WEEK 8

• Midterm Exam

WEEK 9

• Chapter 7. Computing scores in a complete system

WEEK 10

• Chapter 8. Evaluation in information retrieval

WEEK 11

• Chapter 9. Relevance feedback and query expansion

WEEK 12

• Chapter 11. Probabilistic information retrieval

WEEK 13

• Chapter 12. Language models for information retrieval

WEEK 14

• Chapter 13. Text classification and Naïve Bayes

WEEK 15

• Final Exam