1. Retrieval Utilities Relevance feedback Clustering
Passage-based Retrieval
Parsing
N-grams
Thesauri
Semantic Networks
Regression Analysis

2. Relevance Feedback Do the retrieval in multiple steps
User refines the query at each step wrt the results of the previous queries
User tells the IR system which documents are relevant
New terms are added to the query based on the feedback
Term weights may be updated based on the user feedback

3. Relevance Feedback Bypass the user for relevance feedback by
Assuming the top-k results in the ranked list are relevant
Modify the original query as done before

4. Relevance Feedback
Example: “find information surrounding the various conspiracy theories about the assassination of John F. Kennedy” (Example from your text book)
IF the highly ranked document contains the term “Oswald” then this needs to be added to the initial query
If the term “assassination” appears in the top ranked document, then its weight should be increased.

5. Relevance Feedback in Vector Space Model
Q is the original query
R is the set of relevant and S is the set of irrelevant documents selected by the user
|R| = n1, |S| = n2

6. Relevance Feedback in Vector Space Model
Q is the original query
R is the set of relevant and S is the set of irrelevant documents selected by the user
|R| = n1, |S| = n2
In general
The weights are referred to as Rocchio weights

7. Relevance Feedback in Vector Space Model
What if the original query retrieves only non-relevant documents (determined by the user)?
Then increase the weight of the most frequently occurring term in the document collection.

8. Relevance Feedback in Vector Space Model
Result set clustering can be used as a utility for relevance feedback.
Hierarchical clustering can be used for that purpose where the distance is defined by the cosine similarity