Conversing learning as a crowd-powered approach to diminish the effects of semantic drift in bootstrap learning algorithms

Abstract

Internet and social Web made possible the acquisition of information to feed a growing number of Machine Learning applications and, in addition, brought light to the use of crowdsourcing approaches, commonly applied to problems that are easy for humans but difficult for computers to solve, building the crowd-powered systems. In this work, we consider the issue of semantic drift in a bootstrap learning algorithm and propose the novel idea of a crowd-powered approach to diminish the effects of such issue. To put this idea to test we built a hybrid version of the Coupled Pattern Learner, a bootstrap learning algorithm that extract contextual patterns from an unstructured text, and SSCrowd, a component that allows conversation between learning systems and Web users, in an attempt to actively and autonomously look for human supervision by asking people to take part into the knowledge acquisition process, thus using the intelligence of the crowd to improve the learning capabilities of Coupled Pattern Learner. We take advantage of the ease that humans have to understand language in unstructured text, and we show the results of using a hybrid crowd-powered approach to diminish the effects of semantic drift.