The Word is Mightier than the Label Learning without Pointillistic Labels using Data Programming. (arXiv:2108.10921v1 [cs.LG])

Most advanced supervised Machine Learning (ML) models rely on vast amounts of
point-by-point labelled training examples. Hand-labelling vast amounts of data
may be tedious, expensive, and error-prone. Recently, some studies have
explored the use of diverse sources of weak supervision to produce competitive
end model classifiers. In this paper, we survey recent work on weak
supervision, and in particular, we investigate the Data Programming (DP)
framework. Taking a set of potentially noisy heuristics as input, DP assigns
denoised probabilistic labels to each data point in a dataset using a
probabilistic graphical model of heuristics. We analyze the math fundamentals
behind DP and demonstrate the power of it by applying it on two real-world text
classification tasks. Furthermore, we compare DP with pointillistic active and
semi-supervised learning techniques traditionally applied in data-sparse



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