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Query expansion (QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, ...
Relevance feedback is a feature of some information retrieval systems. The idea behind relevance feedback is to take the results that are initially returned from a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query. We can usefully distinguish between three types ...
Indexing and classification methods to assist with information retrieval have a long history dating back to the earliest libraries and collections however systematic evaluation of their effectiveness began in earnest in the 1950s with the rapid expansion in research production across military, government and education and the introduction of computerised catalogues.
Modern information retrieval systems have moved towards eliminating the non-related documents by setting c = 0 and thus only accounting for related documents. Although not all retrieval systems have eliminated the need for non-related documents, most have limited the effects on modified query by only accounting for strongest non-related ...
Their results show that an average query term fails to appear in 30-40% of the documents that are relevant to the user query. They also showed that this probability of mismatch is a central probability in one of the fundamental probabilistic retrieval models, the Binary Independence Model. They developed novel term weight prediction methods ...
The Divergence from randomness model can show the best performance with only a few documents comparing to other query expansion skills. The framework of Divergence from randomness model is very general and flexible. With the query expansion provided for each component, we can apply different technologies in order to get the best performance.
Supports integrated retrieval and ranking with basis on thematic, temporal and geographical aspects. Supports the Lucene standard retrieval model, as well as the more advanced probabilistic retrieval approaches. Supports Rochio Query Expansion. Provides a framework for IR evaluation experiments (e.g. handling CLEF/TREC topics).
In order to interact with Google, first of all, WebCrow needs to compose queries on the basis of the given clues. This is done by query expansion, whose purpose is to convert the clue into a query expressed by a simplified and more appropriate language for Google. The retrieved documents are parsed so as to extract a list of word candidates ...