U-CORE: A Unified Deep Cluster-wise Contrastive Framework for Open Relation Extraction

Challenge:	Existing methods for Relation Extraction (RE) are limited due to the overlap between predefined and undefined relations.
Approach:	They propose a unified framework for both Zero-shot and Unsupervised Relation Extraction tasks by leveraging techniques from Contrastive Learning and Clustering.
Outcome:	The proposed framework improves on three well-known datasets showing an average improvement of 7.35% ARI on Zero-shot ORE tasks and 15.24% ARI for Unsupervised ORE.

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Challenge

Approach

Outcome

HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction (2022.naacl-main)

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Shuliang Liu, Xuming Hu, Chenwei Zhang, Shu’ang Li, Lijie Wen, Philip Yu

Semantic Text Processing

Challenge:	Existing methods to extract relational feature signals from natural language sentences use self-supervised clustering and classification that cause gradual drift problems.
Approach:	They propose a framework that derives hierarchical signals from relational feature space using cross hierarchy attention and effectively optimizes relation representation of sentences under exemplar-wise contrastive learning.
Outcome:	The proposed framework can extract the relationship between entities from natural language sentences without prior knowledge on relation scope or distribution.

RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction (2022.findings-naacl)

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Shusen Wang, Bosen Zhang, Yajing Xu, Yanan Wu, Bo Xiao

Semantic Text Processing

Challenge:	Existing approaches to extract relations require large-scale labeled data.
Approach:	They propose a Relation Contrastive Learning framework to mitigate similar relations and similar entities problems by optimizing a contrastive instance loss with a relation classification loss on seen relations.
Outcome:	The proposed framework can learn subtle difference between instances and achieve better separation between different relation categories in the representation space simultaneously.

Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (2024.emnlp-main)

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Sizhe Zhou, Yu Meng, Bowen Jin, Jiawei Han

Low-Resource NLP

Challenge:	Existing Relation extraction models require extensive annotated training data, which is costly and labor-intensive to collect.
Approach:	They propose a new zero-shot RE task where only relation definitions are provided instead of seen-unseen relation instances.
Outcome:	The proposed task significantly improves cost-effective zero-shot performance by large margins.

UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining (2022.acl-long)

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Jiacheng Li, Jingbo Shang, Julian McAuley

Semantic Text Processing

Challenge:	Existing phrase representation learning methods combine unigram representations in a context-free manner or rely on extensive annotations to learn context-aware knowledge.
Approach:	They propose a novel unsupervised contrastive learning framework for context-aware phrase representations and topic mining.
Outcome:	The proposed framework outperforms the state-of-the-art phrase representation model by 38.2% NMI on four entity clustering tasks.

GLiREL - Generalist Model for Zero-Shot Relation Extraction (2025.naacl-long)

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Jack Boylan, Chris Hokamp, Demian Gholipour Ghalandari

Responsible & Trustworthy NLP

Challenge:	Existing approaches to zero-shot named entity recognition rely on distant supervision and training data for unseen labels.
Approach:	They propose an efficient architecture and training paradigm for zero-shot relation classification . they use a protocol to generate multiple relation labels in a single forward pass .
Outcome:	The proposed architecture and training paradigm achieve state-of-the-art results on the zero-shot relation classification task.

UOREX: Towards Uncertainty-Aware Open Relation Extraction (2025.naacl-long)

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Rebii Jamal, Mounir Ourekouch, Mohammed Erradi

Information Extraction & Text Mining

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.
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Frame First, Then Extract: A Frame-Semantic Reasoning Pipeline for Zero-Shot Relation Triplet Extraction (2025.emnlp-main)

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Zehan Li, Fu Zhang, Wenqing Zhang, Jiawei Li, Zhou Li, Jingwei Cheng, Tianyue Peng

Responsible & Trustworthy NLP

Challenge:	Existing methods to extract triplets for unseen relations rely on costly fine-tuning and lack structured semantic guidance.
Approach:	They propose a framework that adopts a "frame first, then extract" paradigm to extract triplets from unstructured text.
Outcome:	The proposed framework achieves competitive zero-shot performance on multiple benchmarks and can be used to enhance existing extraction methods.

Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction (2024.naacl-short)

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Shilong Li, Ge Bai, Zhang Zhang, Ying Liu, Chenji Lu, Daichi Guo, Ruifang Liu, Sun Yong

Responsible & Trustworthy NLP

Challenge:	Existing methods to extract unseen relations require laborious manual annotation . a new approach uses fine-grained matching to reduce manual annotation cost .
Approach:	They propose an efficient multi-grained matching approach that uses virtual entity matching to reduce manual annotation cost.
Outcome:	The proposed approach outperforms the state-of-the-art methods and achieves inference efficiency and accuracy in zero-shot relation extraction tasks.

Topic-Oriented Open Relation Extraction with A Priori Seed Generation (2024.emnlp-main)

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Linyi Ding, Jinfeng Xiao, Sizhe Zhou, Chaoqi Yang, Jiawei Han

Information Extraction & Text Mining

Challenge:	Existing methods for open relation extraction give sub-optimal results on specific topics.
Approach:	They propose a method that leverages the built-in knowledge of large language models to maintain a dynamic seed relation dictionary for the topic.
Outcome:	The proposed approach empowers better topic-oriented control over the generated relations and improves ORE performance along the five dimensions, especially on specialized and narrow topics.

AlignRE: An Encoding and Semantic Alignment Approach for Zero-Shot Relation Extraction (2024.findings-acl)

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Zehan Li, Fu Zhang, Jingwei Cheng

Semantic Text Processing

Challenge:	Existing prototype-based methods for ZSRE ignore abundant side information and suffer from a significant encoding gap between prototypes and sentences.
Approach:	They propose a framework to encode schema alignment to enhance prototype-based ZSRE methods.
Outcome:	The proposed method outperforms existing methods on FewRel and Wiki-ZSL datasets and exhibits substantially faster performance and reduces the need for extensive manual labor in prototype construction.