# Better Sampling of Negatives for Distantly Supervised Named Entity Recognition

arXiv: https://arxiv.org/abs/2305.13142 · HTML: https://mlsanity.com/paper/2305.13142/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Rethinking Pre-training and Self-training](https://mlsanity.com/paper/2006.06882/) (2020)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [A General Framework for Information Extraction using Dynamic Span Graphs](https://mlsanity.com/paper/1904.03296/) (2019)
- [BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision](https://mlsanity.com/paper/2006.15509/) (2020)
- [Automated Concatenation of Embeddings for Structured Prediction](https://mlsanity.com/paper/2010.05006/) (2020)
- [Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning](https://mlsanity.com/paper/2105.03654/) (2021)
- [Learning Named Entity Tagger using Domain-Specific Dictionary](https://mlsanity.com/paper/1809.03599/) (2018)
- [Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2107.12214/) (2021)
- [Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction](https://mlsanity.com/paper/2205.12696/) (2022)
- [Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning](https://mlsanity.com/paper/1906.01378/) (2019)
- [Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training](https://mlsanity.com/paper/2109.05003/) (2021)
- [Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition](https://mlsanity.com/paper/2012.05426/) (2020)
- [Low-Resource Name Tagging Learned with Weakly Labeled Data](https://mlsanity.com/paper/1908.09659/) (2019)
- [SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data](https://mlsanity.com/paper/1704.06360/) (2017)
- [Noisy-Labeled NER with Confidence Estimation](https://mlsanity.com/paper/2104.04318/) (2021)
- [Named Entity Recognition with Partially Annotated Training Data](https://mlsanity.com/paper/1909.09270/) (2019)
- [De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention](https://mlsanity.com/paper/2106.09233/) (2021)
- [Better Feature Integration for Named Entity Recognition](https://mlsanity.com/paper/2104.05316/) (2021)
- [Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning](https://mlsanity.com/paper/2204.09589/) (2022)
- [Rethinking Negative Sampling for Handling Missing Entity Annotations](https://mlsanity.com/paper/2108.11607/) (2021)
- [Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning](https://mlsanity.com/paper/2110.04429/) (2021)

## Built on by (most cited first, 2 of 2)

- [Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data](https://mlsanity.com/paper/2306.09697/) (2023)
- [Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations](https://mlsanity.com/paper/2505.12454/) (2025)

## Abstract (arXiv)

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.