| Challenge: | Existing methods for relation extraction are limited by their inability to accurately self-assess their performance. |
| Approach: | They propose an approach that effectively models a part of the epistemic uncertainty within OpenRE by preventing overconfident errors. |
| Outcome: | The proposed approach improves OpenRE reliability by preventing overconfident errors. |
Similar Papers
When Phrases Meet Probabilities: Enabling Open Relation Extraction with Cooperating Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing clustering-based open relation extraction methods use pre-trained language models . embeddings from language models are high-dimensional and anisotropic, so there is a gap . |
| Approach: | They propose a framework that makes two LLMs work collaboratively to achieve clustering. |
| Outcome: | The proposed framework outperforms existing methods by 1.4%3.13% on different datasets. |
Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data (D19-1)
Copied to clipboard
| Challenge: | Existing methods to extract relational facts from open domain corpora are time-consuming and human-intensive. |
| Approach: | They propose a framework to learn similarity metrics of relations from labeled data . they propose to transfer relational knowledge to identify novel relations in unlabeled data. |
| Outcome: | Experiments on two real-world datasets show that the proposed framework improves compared with state-of-the-art methods. |
Cluster-aware Pseudo-Labeling for Supervised Open Relation Extraction (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to extract novel relations do not achieve effective knowledge transfer . experimental results show that the proposed method is state-of-the-arts . |
| Approach: | They propose a Cluster-aware Pseudo-Labeling method to improve pseudo-labels quality . they firstly pre-trained the relation models with pre-defined relations to learn them . |
| Outcome: | The proposed method improves the pseudo-labels quality and transfer more knowledge for discovering novel relations. |
A Relation-Oriented Clustering Method for Open Relation Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for open relation extraction (OpenRE) are designed for predefined relations, which cannot deal with new emerging relations in the real world. |
| Approach: | They propose a relation-oriented clustering model that leverages readily available labeled data to learn a relationship-oriented representation. |
| Outcome: | The proposed model reduces error rate by 29.2% and 15.7% on two datasets compared with current SOTA methods. |
Towards a More Generalized Approach in Open Relation Extraction (2025.acl-long)
Copied to clipboard
| Challenge: | Existing OpenRE methods assume unlabeled data is a mixture of known and novel instances. |
| Approach: | They propose a generalized OpenRE setting that considers unlabeled data as a mixture of known and novel instances. |
| Outcome: | The proposed framework outperforms baselines in relation classification and clustering on three benchmark datasets. |
Actively Supervised Clustering for Open Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for Open Relation Extraction (OpenRE) use a two-stage pipeline, which learns relation representations and assignments in the first stage, then manually labels relation for each cluster. |
| Approach: | They propose a method that performs relation learning and relation labeling simultaneously without a significant increase in human effort. |
| Outcome: | The proposed method improves existing SOTA methods by 13.8% and 10.6% on two datasets. |
Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to extract relational facts without pre-defined relation types cluster hard or semi-hard instances into the same relation type. |
| Approach: | They propose a method to learn discriminative representations for open relation extraction by using instance ranking and label calibration strategies. |
| Outcome: | The proposed method outperforms existing methods on two public datasets. |
LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies focus on building models that can only handle predefined relations . however, their reliance on human annotation limits their practicality . |
| Approach: | They propose an open relation extraction framework that can generalize to new relations not encountered during training. |
| Outcome: | The proposed framework can generalize to new relations not encountered during training. |
Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) aims to extract semantic relations between entities in a document. |
| Approach: | They propose a Document-level distant relation extraction framework with unreliable pseudo labels to denoise DS data. |
| Outcome: | The proposed framework outperforms strong baselines on two public datasets. |
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for relation extraction use heuristics or distant-supervised annotations, but distant supervised methods make strong assumptions on entity cooccurrence without sufficient contexts. |
| Approach: | They propose a framework that exploits weak, self-supervised signals by leveraging large pretrained language models for adaptive clustering on contextualized relational features. |
| Outcome: | The proposed framework exploits weak, self-supervised signals on open-domain Relation Extraction . it bootstraps the self-supervised signals by improving contextualized features in relation classification . |