mlsanityData to 13 Oct 2026

Unsupervised Open Relation Extraction

arXiv 1801.07174 ↗First posted 22 Jan 2018cs.CLcited by 14

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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.

Key facts. Cited by 14 papers in the map (data through 13 Oct 2026). First posted on arXiv 22 Jan 2018.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 6; 2024: 2; 2025: 1.

14 papers.

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  1. 2021Prototypical Representation Learning for Relation Extractioncited by 33
  2. 2021A Relation-Oriented Clustering Method for Open Relation Extractioncited by 15
  3. 2022Active Relation Discovery: Towards General and Label-aware Open Relation Extractioncited by 10
  4. 2022A Survey on Open Information Extraction from Rule-based Model to Large Language Modelcited by 8
  5. 2023Towards Open-World Product Attribute Mining: A Lightly-Supervised Approachcited by 6
  6. 2022MORE: A Metric Learning Based Framework for Open-domain Relation Extractioncited by 5
  7. 2023PromptORE -- A Novel Approach Towards Fully Unsupervised Relation Extractioncited by 3
  8. 2024Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discoverycited by 2
  9. 2023Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Settingcited by 1
  10. 2023Open Set Relation Extraction via Unknown-Aware Trainingcited by 1
  11. 2023Actively Supervised Clustering for Open Relation Extractioncited by 1
  12. 2023Siamese Representation Learning for Unsupervised Relation Extractioncited by 1
2 more
  1. 2024Distantly Supervised Morpho-Syntactic Model for Relation Extractioncited by 0
  2. 2025LLM-OREF: An Open Relation Extraction Framework Based on Large Language Modelscited by 0