INST 734
Information Retrieval Systems
Spring 2018
Module 3: Monday February 12 to Sunday February 18
This module takes us through the process of using an index to build a
ranked list of documents in response to a query. The module is
designed to be completed in 12 hours over 7 days. As with every
module, you must complete all componentents of this module by midnight
on the evening of the indicated end date for the module.
Module Checklist
The recommended order for completing the activities in this module is:
View the first reading commentary video.
Note that I suggest that you view this before reading the chapter --
doing so should help you navigate this rather complex chapter more
easily.
View the second reading commentary video.
Its also worth watching this commentary before doing the reading,
although the structure here is not as complex.
Check ELMS to see if you have an additional reading summary assigned to you
this week. If so, complete that summary by midnight Thursday and
submit it using ELMS. Be sure to follow the guidance on page 2 of
the Example Reading
Summary that was provided in Module 1.
Consider joining the optional ELMS chat about student-designed
term projects at 5 PM (EST) on Wednesday February 7. See Project Assignment P4 for details.
A lecture on an
Overview of Text Retrieval Methods from Cheng Zhai's
University of Illinois MOOC on Text Retrieval and Search
Engines. You will need a (free) Coursera account to access
this video.
A lecture on Vector Space Model: Basic Idea from Cheng Zhai's
University of Illinois MOOC on Text Retrieval and Search
Engines. You will need a (free) Coursera account to access
this video.
A lecture on Vector Space Model -- Simplest Instantiation from Cheng Zhai's
University of Illinois MOOC on Text Retrieval and Search
Engines. You will need a (free) Coursera account to access
this video.
A lecture on Vector Space Model: Improved Instantiation from Cheng Zhai's
University of Illinois MOOC on Text Retrieval and Search
Engines. You will need a (free) Coursera account to access
this video.
A lecture on Term Frequency Transformation from Cheng Zhai's
University of Illinois MOOC on Text Retrieval and Search
Engines. You will need a (free) Coursera account to access
this video.
A lecture on Document Length Normalization from Cheng Zhai's
University of Illinois MOOC on Text Retrieval and Search
Engines. You will need a (free) Coursera account to access
this video.
A lecture on Fast
Search from Cheng Zhai's University of Illinois MOOC on
Text Retrieval and Search Engines. You will need a (free)
Coursera account to access this video.
A lecture on Indexing
from Chirag Shah's 2014 online informaion retrieval course at
Rutgers. (this is a large file, not streaming video, so allow
some time).
A lecture on Retrieval
Models from Chirag Shah's 2014 online informaion
retrieval course at Rutgers. (this is a large file, not
streaming video, so allow some time).
Finally, complete Exercise E3. You may
find several resources useful in working on this assignment. perhaps
most useful will be the Boolean Retrieval lecture from Module 2 and
the Similarity-Based Ranking lecture from this module. The lecture
videos by Chris Manning that are linked to from Modules 2 and 3 may
also be useful. Of course, the textbook is an excellent reference as
well. Like all assignments that you are asked to turn in, this is due
at midnight on the last day of the module. Note that you are allowed
to work with other students on exercises, but you must type in the
results yourself (no cut and paste -- see the course description for
details).
Doug Oard
Last modified: Sat Jan 27 17:21:52 2018