Prompt learning for metonymy resolution: Enhancing performance with internal prior knowledge of pre-trained language models-Reference-Cited by-同舟云学术

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Prompt learning for metonymy resolution: Enhancing performance with internal prior knowledge of pre-trained language models

Published:2023-11 Issue: Volume:279 Page:110928
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Zhao Biao^ORCID,Jin Weiqiang^ORCID,Zhang Yu,Huang Subin,Yang Guang^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference45 articles.

1. Addressing syntax-based semantic complementation: Incorporating entity and soft dependency constraints into metonymy resolution;Du;Future Internet,2022

2. Target word masking for location metonymy resolution;Li,2020

3. Vancouver welcomes you! minimalist location metonymy resolution;Gritta,2017

4. Statistical age-of-information optimization for status update over multi-state fading channels;Xiao,2023

5. Locate and label: A two-stage identifier for nested named entity recognition;Shen,2021

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1. CLSESSP: Contrastive learning of sentence embedding with strong semantic prototypes;Knowledge-Based Systems;2024-09

2. A fine-grained self-adapting prompt learning approach for few-shot learning with pre-trained language models;Knowledge-Based Systems;2024-09

3. Multimodal Rumor Detection via Multimodal Prompt Learning;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

4. Multi-modal recursive prompt learning with mixup embedding for generalization recognition;Knowledge-Based Systems;2024-06

5. Leveraging Pretrained Language Models for Enhanced Entity Matching: A Comprehensive Study of Fine-Tuning and Prompt Learning Paradigms;International Journal of Intelligent Systems;2024-04-15

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