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An Efficient Person Name Bipolarization Using KPCA

Authors: Shweta Nigam; Anand Jawdekar;

An Efficient Person Name Bipolarization Using KPCA

Abstract

Many of search engines are habitual to access these personal names aliases for betterment of search. Perfect recognition of existing article is valuable in diverse web related tasks like sentiment analysis, information reclamation, personal name disambiguation, and relation extraction. With the growth of web data many users try to share their knowledge over Internet. Various methods are proposed to personal name bipolarization from the web and web related data. Variety of scheme is also proposed for data mining and information extraction apart from web text analysis. Although the person name bipolarization can be predicted using PCA, but here bipolarization of person name can be predicted using kernel based PCA so that the proposed methodology provides high accuracy as compared to the existing PCA technique.

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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
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