  Although modern named entity recognition (NER) systems show impressive
performance on standard datasets, they perform poorly when presented with noisy
data. In particular, capitalization is a strong signal for entities in many
languages, and even state of the art models overfit to this feature, with
drastically lower performance on uncapitalized text. In this work, we address
the problem of robustness of NER systems in data with noisy or uncertain
casing, using a pretraining objective that predicts casing in text, or a
truecaser, leveraging unlabeled data. The pretrained truecaser is combined with
a standard BiLSTM-CRF model for NER by appending output distributions to
character embeddings. In experiments over several datasets of varying domain
and casing quality, we show that our new model improves performance in uncased
text, even adding value to uncased BERT embeddings. Our method achieves a new
state of the art on the WNUT17 shared task dataset.
