# Unsupervised Open Relation Extraction

arXiv: https://arxiv.org/abs/1801.07174 · HTML: https://mlsanity.com/paper/1801.07174/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2018.

## Built on by (most cited first, 14 of 14)

- [Prototypical Representation Learning for Relation Extraction](https://mlsanity.com/paper/2103.11647/) (2021)
- [A Relation-Oriented Clustering Method for Open Relation Extraction](https://mlsanity.com/paper/2109.07205/) (2021)
- [Active Relation Discovery: Towards General and Label-aware Open Relation Extraction](https://mlsanity.com/paper/2211.04215/) (2022)
- [A Survey on Open Information Extraction from Rule-based Model to Large Language Model](https://mlsanity.com/paper/2208.08690/) (2022)
- [Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach](https://mlsanity.com/paper/2305.18350/) (2023)
- [MORE: A Metric Learning Based Framework for Open-domain Relation Extraction](https://mlsanity.com/paper/2206.00289/) (2022)
- [PromptORE -- A Novel Approach Towards Fully Unsupervised Relation Extraction](https://mlsanity.com/paper/2304.01209/) (2023)
- [Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery](https://mlsanity.com/paper/2401.06327/) (2024)
- [Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting](https://mlsanity.com/paper/2305.13533/) (2023)
- [Open Set Relation Extraction via Unknown-Aware Training](https://mlsanity.com/paper/2306.04950/) (2023)
- [Actively Supervised Clustering for Open Relation Extraction](https://mlsanity.com/paper/2306.04968/) (2023)
- [Siamese Representation Learning for Unsupervised Relation Extraction](https://mlsanity.com/paper/2310.00552/) (2023)
- [Distantly Supervised Morpho-Syntactic Model for Relation Extraction](https://mlsanity.com/paper/2401.10002/) (2024)
- [LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models](https://mlsanity.com/paper/2509.15089/) (2025)

## Abstract (arXiv)

We explore methods to extract relations between named entities from free text in an unsupervised setting. In addition to standard feature extraction, we develop a novel method to re-weight word embeddings. We alleviate the problem of features sparsity using an individual feature reduction. Our approach exhibits a significant improvement by 5.8% over the state-of-the-art relation clustering scoring a F1-score of 0.416 on the NYT-FB dataset.