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A semantic framework for textual data enrichment
2016
Expert systems with applications
In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe
doi:10.1016/j.eswa.2016.03.048
fatcat:i55w6cpyinai5nc3juharay25i