For privacy reasons, personally identi\ufb01able information like age and gender of people is not available publicly. However accurate prediction of such information has important applications in the \ufb01elds of advertising, forensics and business intelligence. Existing methods for this problem have focused on classi\ufb01er learning using content based features like word n-grams and style based features like Part of Speech (POS) n-grams. Two major drawbacks of previous approaches are: (1) they do not consider the semantic relation between words, and (2) they do not handle polysemy. We propose a novel method to address these drawbacks by representing the document using Wikipedia concepts and category information. Experimental results show that classi\ufb01ers learned using such features along with previously used features help us achieve signi\ufb01cantly better accuracy compared to the state-of-the-art methods. Indeed, feature selection shows that our novel features are more effective than previously used content based features.<\/p>\n","protected":false},"excerpt":{"rendered":"
For privacy reasons, personally identi\ufb01able information like age and gender of people is not available publicly. However accurate prediction of such information has important applications in the \ufb01elds of advertising, forensics and business intelligence. 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