  In this paper, we study the named entity recognition (NER) problem under
distant supervision. Due to the incompleteness of the external dictionaries
and/or knowledge bases, such distantly annotated training data usually suffer
from a high false negative rate. To this end, we formulate the Distantly
Supervised NER (DS-NER) problem via Multi-class Positive and Unlabeled (MPU)
learning and propose a theoretically and practically novel CONFidence-based MPU
(Conf-MPU) approach. To handle the incomplete annotations, Conf-MPU consists of
two steps. First, a confidence score is estimated for each token of being an
entity token. Then, the proposed Conf-MPU risk estimation is applied to train a
multi-class classifier for the NER task. Thorough experiments on two benchmark
datasets labeled by various external knowledge demonstrate the superiority of
the proposed Conf-MPU over existing DS-NER methods.
