Constraint Multi-class Positive and Unlabeled Learning for Distantly Supervised Named Entity Recognition
This work addresses the problem of incomplete labeling in DS-NER for NLP practitioners, offering an incremental improvement over previous PU learning methods.
The paper tackles the high false negative rate in distantly supervised named entity recognition (DS-NER) by proposing a constraint multi-class positive and unlabeled learning (CMPU) approach, which demonstrates superior performance on two benchmark datasets compared to existing methods.
Distantly supervised named entity recognition (DS-NER) has been proposed to exploit the automatically labeled training data by external knowledge bases instead of human annotations. However, it tends to suffer from a high false negative rate due to the inherent incompleteness. To address this issue, we present a novel approach called \textbf{C}onstraint \textbf{M}ulti-class \textbf{P}ositive and \textbf{U}nlabeled Learning (CMPU), which introduces a constraint factor on the risk estimator of multiple positive classes. It suggests that the constraint non-negative risk estimator is more robust against overfitting than previous PU learning methods with limited positive data. Solid theoretical analysis on CMPU is provided to prove the validity of our approach. Extensive experiments on two benchmark datasets that were labeled using diverse external knowledge sources serve to demonstrate the superior performance of CMPU in comparison to existing DS-NER methods.