  Real-world text classification tasks often require many labeled training
examples that are expensive to obtain. Recent advancements in machine teaching,
specifically the data programming paradigm, facilitate the creation of training
data sets quickly via a general framework for building weak models, also known
as labeling functions, and denoising them through ensemble learning techniques.
We present a fast, simple data programming method for augmenting text data sets
by generating neighborhood-based weak models with minimal supervision.
Furthermore, our method employs an iterative procedure to identify sparsely
distributed examples from large volumes of unlabeled data. The iterative data
programming techniques improve newer weak models as more labeled data is
confirmed with human-in-loop. We show empirical results on sentence
classification tasks, including those from a task of improving intent
recognition in conversational agents.
