Can the Crowd be Controlled?: A Case Study on Crowd Sourcing and Automatic Validation of Completed Tasks based on User Modeling

Abstract

Annotation is an essential step in the development cycle of many Natural Language Processing (NLP) systems. Lately, crowd-sourcing has been employed to facilitate large scale annotation at a reduced cost. Unfortunately, verifying the quality of the submitted annotations is a daunting task. Existing approaches address this problem either through sampling or redundancy. However, these approaches do have a cost associated with it. Based on the observation that a crowd-sourcing worker returns to do a task that he has done previously, a novel framework for automatic validation of crowd-sourced task is proposed in this paper. A case study based on sentiment analysis is presented to elucidate the framework and its feasibility. The result suggests that validation of the crowd-sourced task can be automated to a certain extent.

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