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.

本文研究用多类正类未标签化学习方法和一种基于置信度的方法来解决远程监督下的命名实体识别问题，并在两个基准数据集上的实验表明该方法优于现有的远程监督命名实体识别方法。