# Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach

arXiv: https://arxiv.org/abs/1707.00166 · HTML: https://mlsanity.com/paper/1707.00166/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2017.

## Built on by (most cited first, 9 of 9)

- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data](https://mlsanity.com/paper/2305.03827/) (2023)
- [Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights](https://mlsanity.com/paper/2104.08936/) (2021)
- [Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction](https://mlsanity.com/paper/2404.09593/) (2024)
- [Locally-Contextual Nonlinear CRFs for Sequence Labeling](https://mlsanity.com/paper/2103.16210/) (2021)
- [Binary Classification with Positive Labeling Sources](https://mlsanity.com/paper/2208.01704/) (2022)
- [Distantly-Supervised Joint Extraction with Noise-Robust Learning](https://mlsanity.com/paper/2310.04994/) (2023)
- [Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning](https://mlsanity.com/paper/2107.12591/) (2021)
- [CrEst: Credibility Estimation for Contexts in LLMs via Weak Supervision](https://mlsanity.com/paper/2506.14912/) (2025)

## Abstract (arXiv)

Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics. These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance. Identifying context information as the backbone of both relation extraction and true label discovery, we adopt embedding techniques to learn the distributed representations of context, which bridges all components with mutual enhancement in an iterative fashion. Extensive experimental results demonstrate the superiority of REHession over the state-of-the-art.