Automatic verbatim coding technology is essential in many contexts in which, either because of the sheer size of the dataset we need to code, or because of demanding time constraints, or because of cost-effectiveness issues, manual coding is not a viable option. However, in some of these contexts the accuracy standards imposed by the customer may be too high for today's automated verbatim coding technology; this means that human coders may need to devote some time to inspecting (and correcting where appropriate) the most problematic autocoded verbatims, with the goal of increasing the accuracy of the coded set. We discuss a software tool for optimising the human coders' work, i.e., a tool that minimizes the amount of human inspection required to reduce the overall error down to a desired level, or that (equivalently) maximises the reduction in the overall error achieved for an available amount of human inspection work.

Optimising human inspection work in automated verbatim coding

Esuli A;Sebastiani F
2014

Abstract

Automatic verbatim coding technology is essential in many contexts in which, either because of the sheer size of the dataset we need to code, or because of demanding time constraints, or because of cost-effectiveness issues, manual coding is not a viable option. However, in some of these contexts the accuracy standards imposed by the customer may be too high for today's automated verbatim coding technology; this means that human coders may need to devote some time to inspecting (and correcting where appropriate) the most problematic autocoded verbatims, with the goal of increasing the accuracy of the coded set. We discuss a software tool for optimising the human coders' work, i.e., a tool that minimizes the amount of human inspection required to reduce the overall error down to a desired level, or that (equivalently) maximises the reduction in the overall error achieved for an available amount of human inspection work.
2014
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
Acti
automated text classification
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/222873
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