Information Retrieval and
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.
Interpolated Recall-Precision Curve:
(for each query)
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
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. You have to use the following set of queries (16 short queries) to evaluate your method; they are the short versions of the original queries employed in Homework #2 and therefore share the same query-document relevance information that was used in Homework #1 and #2.
Homework #4 : Language Modeling for IR
Implement a language modeling (LM) based retrieval model, e.g., unigram, PLSA, LDA and WTM, among others (select just one model to implement). You have to use the following set of queries (16 short queries) to evaluate your method; they are the short versions of the original queries employed in Homework #2 and therefore share the same query-document relevance information that was used in Homework #1 and #2. You will obtain a higher score if you have also performed reference feedback and query expansion, in conjunction with your LM-based retrieval model.