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In this paper, we study the named entity recognition (NER) problem underdistant supervision. Due to the incompleteness of the external dictionariesand/or knowledge bases, such distantly annotated training data usually sufferfrom a high false negative rate. To this end, we formulate the DistantlySupervised 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 oftwo steps. First, a confidence score is estimated for each token of being anentity token. Then, the proposed Conf-MPU risk estimation is applied to train amulti-class classifier for the NER task. Thorough experiments on two benchmarkdatasets labeled by various external knowledge demonstrate the superiority ofthe proposed Conf-MPU over existing DS-NER methods.

Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning