Information Retrieval

Module Description, Aims and Objectives:

Module Description, Aims and Objectives: This module discusses the basic concepts and methods of information retrieval including capturing, representing, storing, organizing, and retrieving unstructured or loosely structured information. The most well-known aspect of information retrieval is document retrieval: the process of indexing and retrieving text documents. However, the field of information retrieval includes almost any type of unstructured or semi-structured data, including newswire stories, transcribed speech, email, blogs, images, or video. Therefore, information retrieval is a critical aspect of Web search engines. This module also serves as the foundation for subsequent modules on the understanding, processing and retrieval of particular web media.

N.B. We will be teaching and using the Python programming language throughout this class. We will using Python 2.6.x (2.6.6 or 2.6.4) instead of the updated Python 3.x, as the NLTK library that we will also be using is currently incompatible with 3.x.

Course Characteristics:

• Modular credits: 4.
• Prerequisites:: CS 2010 Data Structures and Algorithms II or its equivalent. Important: It is highly suggested to have some advanced mathematics background such as probability and statistics, and/or linear algebra. Exceptions to these pre-requisites can be made on a case-by-case basis only. See instructor for details.

• Instructor: Min-Yen KAN, <kanmy@comp.nus.edu.sg> Office: AS6 05-12 (x61885).

  Teaching Assistant: Xiangnan HE, <xiangnan@comp.nus.edu.sg> Office: Computational Linguistics Lab (AS6 04-13).

  Office hours are held (before and after class) on Fridays after class, but more commonly by appointment. Emails to me as a default are assumed to be public, and my replies and your anonymized email will likely be posted to IVLE. Please let me know if you do not want the contents of your email posted; I will be happy to honor your requests.

• Workload: (2-1-0-5-2) Translation:
  2 lecture hours per week
  1 hour of tutorials or labs per week
  5 hours for projects, assignments, fieldwork, etc. per week
  2 hours for preparatory work by a student per week
• Textbooks:
  • Required: Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.
    [ Check LINC for book ]
  • Recommended: Steven Bird, Ewan Klein and Edward Loper, Natural language processing with Python, O'Reilly. 2009.
    [ Check LINC for book ]
  • Recommended: Ian H. Witten, Alistair Moffat, Timothy C. Bell, Managing Gigabytes: Compressing and Indexing Documents and Images, 1999. Second Edition
    [ Check LINC for book ]
  • Recommended: Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Modern Information Retrieval, 1999. First Edition
    [ Check LINC for book ]
• Tutorials: Note: There will only be five tutorial sessions; each tutorial is on a subject related to a homework assignment, and the tutorials are held every other week. We have only two slots that are back-to-back on Thursday afternoon.
  • Tutorial 1: Thursdays 14:00-15:00 (SR7; COM1 #02-07)
  • Tutorial 2: Thursdays 15:00-16:00 13:00-14:00 (SR7; COM1 #02-07)
  • Tutorial 3: Fridays 10:00-11:00 (SR10; COM1 #02-10)
• Final Exam:
  Updated!
  Tuesday, 6 May 2014 Morning, Venue: SoC COM1 HCI Design Studio (COM1 #02-02), This is an open-book exam.

Note to NUS-external visitors: Welcome! If you're a fellow IR course instructor looking for lecture material, you can see the syllabus menu item on the left for a preview. Please contact me if you'd like to use any of my material. Thanks!