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• W49342364 abstract "The rapid construction of supervised text classification models is becoming a pervasive need across many modern applications. To reduce human-labeling bottlenecks, many new statistical paradigms (e.g., active, semi-supervised, transfer and multi-task learning) have been vigorously pursued in recent literature with varying degrees of empirical success. Concurrently, the emergence of Web 2.0 platforms in the last decade has enabled a world-wide, collaborative human effort to construct a massive ontology of concepts with very rich, detailed and accurate descriptions. In this paper we propose a new framework to extract supervisory information from such ontologies and complement it with a shift in human effort from direct labeling of examples in the domain of interest to the much more efficient identification of concept-class associations. Through empirical studies on text categorization problems using the Wikipedia ontology, we show that this shift allows very high-quality models to be immediately induced at virtually no cost." @default.
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• W49342364 date "2011-07-16" @default.
• W49342364 modified "2023-09-26" @default.
• W49342364 title "Concept labeling: building text classifiers with minimal supervision" @default.
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• W49342364 doi "https://doi.org/10.5591/978-1-57735-516-8/ijcai11-208" @default.
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