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 . |
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| Challenge: | Existing methods to extract relational facts from open domain corpora are time-consuming and human-intensive. |
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| Challenge: | Existing methods to extract relational feature signals from natural language sentences use self-supervised clustering and classification that cause gradual drift problems. |
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Jun Zhao, Xin Zhao, WenYu Zhan, Qi Zhang, Tao Gui, Zhongyu Wei, Yun Wen Chen, Xiang Gao, Xuanjing Huang
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| Challenge: | Existing frameworks for relation extraction (RE) are limited due to lack of implementation details. |
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| Challenge: | Existing OpenRE methods assume unlabeled data is a mixture of known and novel instances. |
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| Challenge: | Existing relation extraction models rely on supervised machine learning, but many datasets are incompletely annotated, causing false negatives and errors during inference stage. |
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More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)
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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun
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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. |
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A Relation-Oriented Clustering Method for Open Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for open relation extraction (OpenRE) are designed for predefined relations, which cannot deal with new emerging relations in the real world. |
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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. |
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