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Automatic Query Reformulation Using Classification in Web Searching

Authors: Ralla Suresh; Kurmachalam Ajay Kumar;

Automatic Query Reformulation Using Classification in Web Searching

Abstract

The search query is a set of words or phrases a user enters when looking for information on a specific topic or subject. Formulating a search query is a challenging task for most of users because they are required to express their anomalous states of knowledge. In the query reformulation stage, users modify their initial queries and submit new ones that more accurately reflect their information needs. Classification and Prediction are two forms of query analysis that can be used to extract models describing frequently used query classes or to predict the reformulation for the query.. An ideal solution might be that the system automatically generates a concise and informative summary for each perspective of the query. In our approach a model is constructed by analyzing queries described by the frequency of search, query is assumed to belong to a predefined class.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold