# Improving Neural Relation Extraction with Positive and Unlabeled Learning

arXiv: https://arxiv.org/abs/1911.12556 · HTML: https://mlsanity.com/paper/1911.12556/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [Multimodal Learning on Graphs for Disease Relation Extraction](https://mlsanity.com/paper/2203.08893/) (2022)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling](https://mlsanity.com/paper/2210.08709/) (2022)
- [Knodle: Modular Weakly Supervised Learning with PyTorch](https://mlsanity.com/paper/2104.11557/) (2021)
- [A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling](https://mlsanity.com/paper/2306.14806/) (2023)
- [Improving Distantly Supervised Relation Extraction by Natural Language Inference](https://mlsanity.com/paper/2208.00346/) (2022)
- [Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering](https://mlsanity.com/paper/2108.09689/) (2021)

## Abstract (arXiv)

We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning. This approach first applies reinforcement learning to decide whether a sentence is positive to a given relation, and then positive and unlabeled bags are constructed. In contrast to most previous studies, which mainly use selected positive instances only, we make full use of unlabeled instances and propose two new representations for positive and unlabeled bags. These two representations are then combined in an appropriate way to make bag-level prediction. Experimental results on a widely used real-world dataset demonstrate that this new approach indeed achieves significant and consistent improvements as compared to several competitive baselines.