Learning to Denoise Distantly-Labeled Data for Entity Typing

NAACL 2019 Yasumasa OnoeGreg Durrett

Distantly-labeled data can be used to scale up training of statistical models, but it is typically noisy and that noise can vary with the distant labeling technique. In this work, we propose a two-stage procedure for handling this type of data: denoise it with a learned model, then train our final model on clean and denoised distant data with standard supervised training... (read more)

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