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Most advanced supervised Machine Learning (ML) models rely on vast amounts ofpoint-by-point labelled training examples. Hand-labelling vast amounts of datamay be tedious, expensive, and error-prone. Recently, some studies haveexplored the use of diverse sources of weak supervision to produce competitiveend model classifiers. In this paper, we survey recent work on weaksupervision, and in particular, we investigate the Data Programming (DP)framework. Taking a set of potentially noisy heuristics as input, DP assignsdenoised probabilistic labels to each data point in a dataset using aprobabilistic graphical model of heuristics. We analyze the math fundamentalsbehind DP and demonstrate the power of it by applying it on two real-world textclassification tasks. Furthermore, we compare DP with pointillistic active andsemi-supervised learning techniques traditionally applied in data-sparsesettings.

The Word is Mightier than the Label: Learning without Pointillistic Labels using Data Programming