Reading Notes for Lecture 7

Task:
1. IIR Chapter 9.
2. Xu, J. and Croft, W. B. 2000. Improving the effectiveness of information retrieval with local context analysis. ACM Trans. Inf. Syst. 18, 1 (Jan. 2000), 79-112.
3. Wang, X., Fang, H., and Zhai, C. 2008. A study of methods for negative relevance feedback. In Proceedings of the 31st Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Singapore, Singapore, July 20 - 24, 2008). SIGIR '08. ACM, New York, NY, 219-226.
4. Donna Harman, Relevance feedback revisited. Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval. Pages: 1 - 10. Copenhagen, Denmark. 1992.

The idea of relevance feedback is to involve the user in the retrieval process so as to improve the final result set. In particular, the user gives feedback on the relevance of documents in an initial set of results. 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 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.

Relevance feedback can go through one or more iterations of this sort. The process exploits the idea that it may be difficult to formulate a good query when you don't know the collection well, but it is easy to judge particular documents, and so it makes sense to engage in iterative query refinement of this sort. In such a scenario, relevance feedback can also be effective in tracking a user's evolving information need: seeing some documents may lead users to refine their understanding of the information they are seeking.
The idea of relevance feedback ( ) is to involve the user in the retrieval process so as to improve the final result set. In particular, the user gives feedback on the relevance of documents in an initial set of results. 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 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.

Relevance feedback can go through one or more iterations of this sort. The process exploits the idea that it may be difficult to formulate a good query when you don't know the collection well, but it is easy to judge particular documents, and so it makes sense to engage in iterative query refinement of this sort. In such a scenario, relevance feedback can also be effective in tracking a user's evolving information need: seeing some documents may lead users to refine their understanding of the information they are seeking.
The idea of relevance feedback ( ) is to involve the user in the retrieval process so as to improve the final result set. In particular, the user gives feedback on the relevance of documents in an initial set of results. 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 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.

Relevance feedback can go through one or more iterations of this sort. The process exploits the idea that it may be difficult to formulate a good query when you don't know the collection well, but it is easy to judge particular documents, and so it makes sense to engage in iterative query refinement of this sort. In such a scenario, relevance feedback can also be effective in tracking a user's evolving information need: seeing some documents may lead users to refine their understanding of the information they are seeking.
Pseudo relevance feedback,also known as blind relevance feedback , provides a method for automatic local analysis. It automates the manual part of relevance feedback, so that the user gets improved retrieval performance without an extended interaction.
Query expansion(QE) is the process of reformulating a seed query to improve retrieval performance in information retrieval operations. In the context of web search engines, query expansion involves evaluating a user's input (what words were typed into the search query area, and sometimes other types of data) and expanding the search query to match additional documents. Query expansion involves techniques such as:

• Finding synonyms of words, and searching for the synonyms as well
• Finding all the various morphological forms of words by stemming each word in the search query
• Fixing spelling errors and automatically searching for the corrected form or suggesting it in the results
• Re-weighting the terms in the original query

Query expansion is a methodology studied in the field of computer science, particularly within the realm of natural language processing and information retrieval.
Q1:
I am curious about the realization of finding synonyms, if it is realized by creating synonyms list for a word? or by other ways?