Active Learning from Crowd in Document Screening
This work addresses the challenge of efficient document screening for researchers or organizations with limited annotation resources, presenting an incremental improvement in active learning methods.
The paper tackles the problem of document screening by combining crowdsourcing and machine learning to efficiently train multiple classifiers under a limited budget, proposing an objective-aware sampling technique that minimizes overall classification errors and significantly outperforms existing active learning strategies.
In this paper, we explore how to efficiently combine crowdsourcing and machine intelligence for the problem of document screening, where we need to screen documents with a set of machine-learning filters. Specifically, we focus on building a set of machine learning classifiers that evaluate documents, and then screen them efficiently. It is a challenging task since the budget is limited and there are countless number of ways to spend the given budget on the problem. We propose a multi-label active learning screening specific sampling technique -- objective-aware sampling -- for querying unlabelled documents for annotating. Our algorithm takes a decision on which machine filter need more training data and how to choose unlabeled items to annotate in order to minimize the risk of overall classification errors rather than minimizing a single filter error. We demonstrate that objective-aware sampling significantly outperforms the state of the art active learning sampling strategies.