Better Sampling of Negatives for Distantly Supervised Named Entity Recognition

TL;DR

This paper introduces a simple negative sampling method for distantly supervised NER that improves performance by selecting negatives similar to positives, addressing false negatives in noisy datasets.

Contribution

It proposes a straightforward negative sampling approach based on similarity to positives, enhancing distantly supervised NER without needing a classifier for weights.

Findings

Consistent performance improvements on four datasets.

Effective differentiation between true negatives and false negatives.

Highlights importance of negative sample selection in noisy data.

Abstract

Distantly supervised named entity recognition (DS-NER) has been proposed to exploit the automatically labeled training data instead of human annotations. The distantly annotated datasets are often noisy and contain a considerable number of false negatives. The recent approach uses a weighted sampling approach to select a subset of negative samples for training. However, it requires a good classifier to assign weights to the negative samples. In this paper, we propose a simple and straightforward approach for selecting the top negative samples that have high similarities with all the positive samples for training. Our method achieves consistent performance improvements on four distantly supervised NER datasets. Our analysis also shows that it is critical to differentiate the true negatives from the false negatives.