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Automatic Web Query Classification Using Large Unlabeled Web Pages

Authors: Jingbo Yu; Na Ye;

Automatic Web Query Classification Using Large Unlabeled Web Pages

Abstract

In this paper, a novel and simple method is employed to automatically construct domain knowledge base for query classification from large-scale Web pages. Besides, using context as the feature of words, the resource of relevant words is built automatically in order to extend the user's query. On the basis of domain knowledge base and extension of the query using relevant words, satisfactory performance in query classification is achieved. Experimental results demonstrate that our method achieves precision of 77.68% and recall of 75.34% in Chinese query classification. In English experiments, in spite of the scarcity of English Web pages and absence of stemming, precision achieves 58.83% and recall achieves 54.13%, which is a great improvement compared to state-of-the-art query classification algorithms.

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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!
3
Average
Average
Average
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