  Named Entity Recognition (NER) is one of the first stages in deep language
understanding yet current NER models heavily rely on human-annotated data. In
this work, to alleviate the dependence on labeled data, we propose a Local
Additivity based Data Augmentation (LADA) method for semi-supervised NER, in
which we create virtual samples by interpolating sequences close to each other.
Our approach has two variations: Intra-LADA and Inter-LADA, where Intra-LADA
performs interpolations among tokens within one sentence, and Inter-LADA
samples different sentences to interpolate. Through linear additions between
sampled training data, LADA creates an infinite amount of labeled data and
improves both entity and context learning. We further extend LADA to the
semi-supervised setting by designing a novel consistency loss for unlabeled
data. Experiments conducted on two NER benchmarks demonstrate the effectiveness
of our methods over several strong baselines. We have publicly released our
code at https://github.com/GT-SALT/LADA.
