Relevance feedback and query expansion
Methods for synonymy issue:
- Query expansion/reformulation with a thesaurus or WordNet
- Query expansion via automatic thesaurus generation
- Techniques like spelling correction
- Relevance feedback
- Pseudo relevance feedback, also known as Blind relevance feedback
- Indirect relevance feedback
relevance feedback (RF) - involvement of the user in the retrieval process to improve the final result set.
- 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 nonrelevant.
- The system computes a better representation of the information need based on the user feedback.
- The system displays a revised set of retrieval results.
The Rocchio algorithm for relevance feedback
Relevance feedback can improve both recall and precision.
Pseudo relevance feedback, also known as blind relevance feedback, provides a method for automatic local analysis. It automates the manual part of rele- vance feedback, so that the user gets improved retrieval performance with- out an extended interaction.
Implicit feedback is less reliable than explicit feedback, but is more useful than pseudo relevance feedback, which contains no evidence of user judgments.
Global methods for query reformulation
Vocabulary tools for query reformulation
Query expansion:
- Use of a controlled vocabulary that is maintained by human editors.
- A manual thesaurus.
- An automatically derived thesaurus.
- Query reformulations based on query log mining.
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