Reading notes for Unit 7

Relevance feedback and query expansion

query refinement

Global methods include:

• Query expansion/reformulationwith a thesaurus orWordNet

• Query expansion via automatic thesaurus generation

• Techniques like spelling correction

Local methods

• Relevance feedback

• Pseudo relevance feedback, also known as Blind relevance feedback

• (Global) indirect relevance feedback

The idea of relevance feedback (RF) is to involve the user in the retrieval process so as to improve the final result set.

The basic procedure is:

• The user issues a (short, simple) query.

• The system returns an initial set of retrieval results.

• The user marks some returned documents as relevant or non-relevant.

• The system computes a better representation of the information need based on the user feedback.

• The system displays a revised set of retrieval results.

Rocchio algorithm

The Rocchio Algorithm is the classic algorithm for implementing relevance feedback. It models a way of incorporating relevance feedback information into the vector space model.

Relevance feedback can improve both recall and precision.

Probabilistic relevance feedback

Rather than reweighting the query in a vector space, if a user has told us some relevant and nonrelevant documents, then we can proceed to build a classifier.

The use only collection statistics and information about the term distribution within the documents judged relevant. They preserve no memory of the original query.

Cases where relevance feedback alone is not sufficient include:

·         Misspellings.

·         Cross-language information retrieval.

·         Mismatch of searcher’s vocabulary versus collection vocabulary.

Secondly, the relevance feedback approach requires relevant documents to be similar to each other.