Papers with Open relation extraction
Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data (D19-1)
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| 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. |
Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration (2022.findings-naacl)
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| 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. |
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction (2020.emnlp-main)
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| 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 . |
UOREX: Towards Uncertainty-Aware Open Relation Extraction (2025.naacl-long)
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| 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. |
Element Intervention for Open Relation Extraction (2021.acl-long)
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| Challenge: | Current OpenRE models are often trained on the datasets generated from distant supervision, which often results in instability and makes the model easily collapsed. |
| Approach: | They propose to use a causal model to identify relation instances referring to the same relation . they propose to perform Element Interventions on context and entities respectively . |
| Outcome: | The proposed method outperforms existing methods and is robust across datasets. |
Open Hierarchical Relation Extraction (2021.naacl-main)
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| Challenge: | Existing OpenRE methods cast different relation types in isolation without considering their hierarchical dependency. |
| Approach: | They propose a framework to establish bidirectional connections between OpenRE and relation hierarchies by integrating hierarchy information into relation representations. |
| Outcome: | The proposed framework outperforms state-of-the-art models on relation clustering and hierarchy expansion. |