Information Retrieval and Extraction
Spring 2018

Homework Webpage


Homework #1 :Evaluation Metrics for IR

The the query-document relevance information (AssessmentTrainSet.txt) for a set of queries (16 queries) and 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

Example 2: (Non-interpolated) Mean Average Precision


3. Normalized Discounted Cumulated Gain (NDCG) :

Homework #2 : 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 (or Probabilistic Model, Generalized Vector Space Model, Latent Semantic Analysis, Language Model, etc.). The query-document   relevance information is in "AssessmentTrainSet.txt".  You should evaluated you system with the two measures described in HW#1.


Homework #3 : Relevance Feedback and Query Expansion

Try to Integrate the function of query expansion and term re-weighting into your retrieval system that has been built in Homework #2. Either (automatic) reference feedback or local analysis can be adopted as the strategy for it, but local analysis is preferred.

Alternatively, you may use the following set of queries (16 short queries) to evaluate your method; it consists the short versions of the original queries employed in Homework #2 and therefore shares the same query-document relevance information that was used in Homework #1 and #2.