Human-in-the-loop web resource classification

Abstract

Engaging humans in the resolution of classification tasks has been shown to be effective especially when digital resources are considered, with complex features to be abstracted for an automated procedure, like images or multimedia web resources. In this paper, we propose the HC2 crowdclustering approach for unsupervised classification of web resources, by allowing the classification categories to dynamically emerge from the crowd. In HC2, crowd workers actively participate to clustering activities (i) by resolving tasks in which they are asked to visually recognize groups of similar resources and (ii) by labeling recognized clusters with prominent keywords. To increase flexibility, HC2 can be interactively configured to dynamically set the balance between human engagement and automated procedures in cluster formation, according to the kind and nature of resources to be classified. For experimentation and evaluation, the HC2 approach has been deployed on the Argo platform providing crowdsourcing techniques for consensus-based task execution.