Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning

Understand

In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries.

• To this end, we formulate the task as a positive-unlabeled (PU) learning problem and accordingly propose a novel PU learning algorithm to perform the task.
• We prove that the proposed algorithm can unbiasedly and consistently estimate the task loss as if there is fully labeled data.
• A key feature of the proposed method is that it does not require the dictionaries to label every entity within a sentence, and it even does not require the dictionaries to label all of the words constituting an entity.