# Improving distant supervision using inference learning

arXiv: https://arxiv.org/abs/1509.03739 · HTML: https://mlsanity.com/paper/1509.03739/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2015.

## Built on by (most cited first, 3 of 3)

- [Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED](https://mlsanity.com/paper/2204.07980/) (2022)
- [Revisiting the Negative Data of Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2105.10158/) (2021)
- [WebRED: Effective Pretraining And Finetuning For Relation Extraction On The Web](https://mlsanity.com/paper/2102.09681/) (2021)

## Abstract (arXiv)

Distant supervision is a widely applied approach to automatic training of relation extraction systems and has the advantage that it can generate large amounts of labelled data with minimal effort. However, this data may contain errors and consequently systems trained using distant supervision tend not to perform as well as those based on manually labelled data. This work proposes a novel method for detecting potential false negative training examples using a knowledge inference method. Results show that our approach improves the performance of relation extraction systems trained using distantly supervised data.