Combining machine intelligence with the skills of human crowds produces powerful systems for analysing data and meeting complexing information needs. For the TREC crowdsourcing challenge, we demonstrated a Bayesian probabilistic method for combining the responses of a crowd with features extracted using natural language processing, allowing a small number of crowdsourced labels to train a classifier. This automation reduces the cost of crowdsourcing and expands the size of datasets we can manage.  Furthermore, Bayesian modelling approaches allow us to select optimal pairs of workers and tasks, vastly increasing the efficiency and speed of learning. 

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