Information Retrieval and Extraction
Fall 2003

Homework Webpage

Note: 1. If you have any problems, please contact me directly or contact the TA.
         2. Don't download the experimental materials unless you take this course.
             (parts of the materials are under copyright protection)

TA: Roger Kuo (郭人瑋)  
       Tel: 2932-2411 ext 208


Homework #1 :Evaluation Measures

Homework #2 :Classic Retrieval Models

Homework #3 :Query Expansion and Term Reweighting


Homework #1 :Evaluation Measures

The the query-document relevance information (AssessmentTrainSet.txt) for a set of queries (16 queries) on a collection of 2,265 documents is provided. An IR model is then tested on this query set  and save the corresponding ranking results in a file (ResultsTrainSet.txt) . Please evaluate the overall model performance using the following two measures.

1. Interpolated Recall-Precision Curve: 
    (for each query)

          (overall performance)

2. (Non-interpolated) Mean Average Precision:


, where "non-interpolated average precision" is "average precision at seen relevant documents" introduced in the textbook.

Example 1: Interpolated Recall-Precision Curve (By Roger Kuo, Spring 2003)

Example 2: (Non-interpolated) Mean Average Precision (By Li-Der Huang, Spring 2003)



Homework #2 :Classic Retrieval Models

A  set of text queries (16 queries) and a collection of text documents ( 2,265 documents) is provided, in which each word is represented as a number except that the number "-1" is a delimiter. Implement an information retrieval system based on the Vector (Space) Model as well as different term weighting schemes. The query-document relevance information is in "AssessmentTrainSet.txt".  You should evaluated you system with the two measures described in HW#1.


Homework #3 :Query Expansion and Term Reweighting

You should augment the function of query expansion and term reweighting into your retrieval system that has been built in HW#2. Either (automatic) reference feedback or local analysis can be adopted as the strategy for it, but local analysis is preferred.