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Relation extraction (RE) is an important task that aims to identify the relationships between entities in texts.
The automatic content extraction (ace) program-tasks, data, and evaluation
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Ace 2005 multilingual training corpus-linguistic data consortium
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Modeling relations and their mentions without labeled text
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Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports
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Relation classification via recurrent neural network
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Model-agnostic meta-learning for fast adaptation of deep networks
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Zero-shot relation extraction via reading comprehension
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Prototypical networks for few-shot learning
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Position-aware attention and supervised data improve slot filling
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FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, et al · 2018
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Improving distantly supervised relation extraction using word and entity based attention
Sharmistha Jat, Siddhesh Khandelwal, and Partha Talukdar · 2018
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Yi Luan, Luheng He, et al · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, et al · 2019
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Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals
Iris Hendrickx, Su Nam Kim, et al · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, et al · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, et al · 2019
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A hierarchical framework for relation extraction with reinforcement learning
Ryuichi Takanobu, Tianyang Zhang, et al · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, et al · 2020
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Learning from context or names? an empirical study on neural relation extraction
Hao Peng, Tianyu Gao, et al · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Jeff Rasley, Samyam Rajbhandari, et al · 2020
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Two are better than one: Joint entity and relation extraction with table-sequence encoders
Jue Wang and Wei Lu · 2020
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A novel cascade binary tagging framework for relational triple extraction
Zhepei Wei, Jianlin Su, et al · 2020
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Simple and effective few-shot named entity recognition with structured nearest neighbor learning
Yi Yang and Arzoo Katiyar · 2020
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Bridging text and knowledge with multi-prototype embedding for few-shot relational triple extraction
Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, et al · 2022
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Deepstruct: Pretraining of language models for structure prediction
Chenguang Wang, Xiao Liu, et al · 2022
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, et al · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, et al · 2022
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Better few-shot relation extraction with label prompt dropout
Peiyuan Zhang and Wei Lu · 2022
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Hierarchical verbalizer for few-shot hierarchical text classification
Ke Ji, Yixin Lian, et al · 2023
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Haiyang Yu, Ningyu Zhang, et al · 2020
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ZS-BERT: Towards zero-shot relation extraction with attribute representation learning
Chih-Yao Chen and Cheng-Te Li · 2021
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Few-shot event detection with prototypical amortized conditional random field
Xin Cong, Shiyao Cui, et al · 2021
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Exploring task difficulty for few-shot relation extraction
Jiale Han, Bo Cheng, and Wei Lu · 2021
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Label verbalization and entailment for effective zero-and few-shot relation extraction
Oscar Sainz, Oier Lopez de Lacalle, et al · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, et al · 2021
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Large language models are few-shot clinical information extractors
Monica Agrawal, Stefan Hegselmann, et al · 2022
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Zero-shot triplet extraction by template infilling
Bosung Kim, Hayate Iso, et al · 2023
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Revisiting large language models as zero-shot relation extractors
Guozheng Li, Peng Wang, and Wenjun Ke · 2023
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Chain of thought with explicit evidence reasoning for few-shot relation extraction
Xilai Ma, Jing Li, and Min Zhang · 2023
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Large language model is not a good few-shot information extractor, but a good reranker for hard samples!
Yubo Ma, Yixin Cao, et al · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, et al · 2023
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GPT-RE: In-context learning for relation extraction using large language models
Zhen Wan, Fei Cheng, et al · 2023
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fmlre: a low-resource relation extraction model based on feature mapping similarity calculation
Peng Wang, Tong Shao, et al · 2023
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Pascore: a chinese overlapping relation extraction model based on global pointer annotation strategy
Peng Wang, Jiafeng Xie, et al · 2023
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Instructuie: Multi-task instruction tuning for unified information extraction
Xiao Wang, Weikang Zhou, et al · 2023
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Mutually guided few-shot learning for relational triple extraction
Chengmei Yang, Shuai Jiang, et al · 2023
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HyperNetwork-based decoupling to improve model generalization for few-shot relation extraction
Liang Zhang, Chulun Zhou, et al · 2023
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RE-matching: A fine-grained semantic matching method for zero-shot relation extraction
Jun Zhao, WenYu Zhan, et al · 2023
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Unlocking instructive in-context learning with tabular prompting for relational triple extraction
Guozheng Li, Wenjun Ke, et al · 2024
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Towards continual knowledge graph embedding via incremental distillation
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Ontofact: Unveiling fantastic fact-skeleton of llms via ontology-driven reinforcement learning
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