Information Retrieval and Extraction
Spring 2016

Homework Webpage

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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

             mAP=0.63787418

3. Normalized Discounted Cumulated Gain (NDCG) :

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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.

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Homework #3 : Relevance Feedback and Query Expansion

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.