  Named Entity Recognition (NER) performance often degrades rapidly when
applied to target domains that differ from the texts observed during training.
When in-domain labelled data is available, transfer learning techniques can be
used to adapt existing NER models to the target domain. But what should one do
when there is no hand-labelled data for the target domain? This paper presents
a simple but powerful approach to learn NER models in the absence of labelled
data through weak supervision. The approach relies on a broad spectrum of
labelling functions to automatically annotate texts from the target domain.
These annotations are then merged together using a hidden Markov model which
captures the varying accuracies and confusions of the labelling functions. A
sequence labelling model can finally be trained on the basis of this unified
annotation. We evaluate the approach on two English datasets (CoNLL 2003 and
news articles from Reuters and Bloomberg) and demonstrate an improvement of
about 7 percentage points in entity-level $F_1$ scores compared to an
out-of-domain neural NER model.
