title: Construction of Machine-Labeled Data for Improving Named Entity Recognition by Transfer Learning
abstract: Deep neural networks (DNNs) require a large amount of manually labeled training data to make significant achievements. However, manual labeling is laborious and costly. In this study, we propose a method for automatically generating training data and effectively using the generated data to reduce the labeling cost. The generated data (called “machine-labeled data”) is generated using a bagging-based bootstrapping approach. However, using the machine-labeled data does not guarantee high performan
journal: IEEE Access