  We present skweak, a versatile, Python-based software toolkit enabling NLP
developers to apply weak supervision to a wide range of NLP tasks. Weak
supervision is an emerging machine learning paradigm based on a simple idea:
instead of labelling data points by hand, we use labelling functions derived
from domain knowledge to automatically obtain annotations for a given dataset.
The resulting labels are then aggregated with a generative model that estimates
the accuracy (and possible confusions) of each labelling function. The skweak
toolkit makes it easy to implement a large spectrum of labelling functions
(such as heuristics, gazetteers, neural models or linguistic constraints) on
text data, apply them on a corpus, and aggregate their results in a fully
unsupervised fashion. skweak is especially designed to facilitate the use of
weak supervision for NLP tasks such as text classification and sequence
labelling. We illustrate the use of skweak for NER and sentiment analysis.
skweak is released under an open-source license and is available at:
https://github.com/NorskRegnesentral/skweak

Computation and Language;Artificial Intelligence;Machine Learning