research-article

KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction

Authors:

Huajun ChenAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2778 - 2788

https://doi.org/10.1145/3485447.3511998

Published: 25 April 2022 Publication History

Abstract

Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into the input and transform a classification task into a masked language modeling problem. However, for relation extraction, determining an appropriate prompt template requires domain expertise, and it is cumbersome and time-consuming to obtain a suitable label word. Furthermore, there exists abundant semantic and prior knowledge among the relation labels that cannot be ignored. To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt). Specifically, we inject latent knowledge contained in relation labels into prompt construction with learnable virtual type words and answer words. Then, we synergistically optimize their representation with structured constraints. Extensive experimental results on five datasets with standard and low-resource settings demonstrate the effectiveness of our approach. Our code and datasets are available in GitHub1 for reproducibility.

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Index Terms

KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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https://doi.org/10.1109/ACCESS.2025.3532458
Zhong SGatti EHardwick JRibul MCho YObrist M(2025) LLM-mediated domain-specific voice agents: the case of TextileBot Behaviour & Information Technology10.1080/0144929X.2025.2456667(1-33)Online publication date: 3-Feb-2025
https://doi.org/10.1080/0144929X.2025.2456667
Zhang ZYang YChen B(2025)A prompt tuning method based on relation graphs for few-shot relation extractionNeural Networks10.1016/j.neunet.2025.107214185(107214)Online publication date: May-2025
https://doi.org/10.1016/j.neunet.2025.107214
Zhang LZheng N(2025)DIRel: Joint relational triple extraction through dual implicit relationNeurocomputing10.1016/j.neucom.2025.129374624(129374)Online publication date: Apr-2025
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Li ZZhu YHua CLi YYuan YQiang J(2025)Domain adaptation for textual adversarial defense via prompt-tuningNeurocomputing10.1016/j.neucom.2024.129192620(129192)Online publication date: Mar-2025
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Xia YTong ZWang LLiu QWu SZhang X(2025) MPE : Learning meta-prompt with entity-enhanced semantics for few-shot named entity recognition Neurocomputing10.1016/j.neucom.2024.129031620(129031)Online publication date: Mar-2025
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Yang WChen YXu JQin YChen P(2025)Automatically learning linguistic structures for entity relation extractionInformation Processing & Management10.1016/j.ipm.2024.10390462:1(103904)Online publication date: Jan-2025
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