  Weak supervision has shown promising results in many natural language
processing tasks, such as Named Entity Recognition (NER). Existing work mainly
focuses on learning deep NER models only with weak supervision, i.e., without
any human annotation, and shows that by merely using weakly labeled data, one
can achieve good performance, though still underperforms fully supervised NER
with manually/strongly labeled data. In this paper, we consider a more
practical scenario, where we have both a small amount of strongly labeled data
and a large amount of weakly labeled data. Unfortunately, we observe that
weakly labeled data does not necessarily improve, or even deteriorate the model
performance (due to the extensive noise in the weak labels) when we train deep
NER models over a simple or weighted combination of the strongly labeled and
weakly labeled data. To address this issue, we propose a new multi-stage
computational framework -- NEEDLE with three essential ingredients: (1) weak
label completion, (2) noise-aware loss function, and (3) final fine-tuning over
the strongly labeled data. Through experiments on E-commerce query NER and
Biomedical NER, we demonstrate that NEEDLE can effectively suppress the noise
of the weak labels and outperforms existing methods. In particular, we achieve
new SOTA F1-scores on 3 Biomedical NER datasets: BC5CDR-chem 93.74,
BC5CDR-disease 90.69, NCBI-disease 92.28.

Computation and Language;Artificial Intelligence;Machine Learning