[HTML][HTML] Ptr: Prompt tuning with rules for text classification

X Han, W Zhao, N Ding, Z Liu, M Sun - AI Open, 2022 - Elsevier
Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-
trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved …

Document-level relation extraction with adaptive focal loss and knowledge distillation

Q Tan, R He, L Bing, HT Ng - ar**
N Ding, Y Chen, X Han, G Xu, P **e, HT Zheng… - arxiv preprint arxiv …, 2021 - arxiv.org
As an effective approach to tune pre-trained language models (PLMs) for specific tasks,
prompt-learning has recently attracted much attention from researchers. By using\textit …

Multimodal relation extraction with efficient graph alignment

C Zheng, J Feng, Z Fu, Y Cai, Q Li, T Wang - Proceedings of the 29th …, 2021 - dl.acm.org
Relation extraction (RE) is a fundamental process in constructing knowledge graphs.
However, previous methods on relation extraction suffer sharp performance decline in short …

Virtual prompt pre-training for prototype-based few-shot relation extraction

K He, Y Huang, R Mao, T Gong, C Li… - Expert systems with …, 2023 - Elsevier
Prompt tuning with pre-trained language models (PLM) has exhibited outstanding
performance by reducing the gap between pre-training tasks and various downstream …

An improved baseline for sentence-level relation extraction

W Zhou, M Chen - arxiv preprint arxiv:2102.01373, 2021 - arxiv.org
Sentence-level relation extraction (RE) aims at identifying the relationship between two
entities in a sentence. Many efforts have been devoted to this problem, while the best …

ERICA: Improving entity and relation understanding for pre-trained language models via contrastive learning

Y Qin, Y Lin, R Takanobu, Z Liu, P Li, H Ji… - arxiv preprint arxiv …, 2020 - arxiv.org
Pre-trained Language Models (PLMs) have shown superior performance on various
downstream Natural Language Processing (NLP) tasks. However, conventional pre-training …

Exploring task difficulty for few-shot relation extraction

J Han, B Cheng, W Lu - arxiv preprint arxiv:2109.05473, 2021 - arxiv.org
Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with
merely a handful of annotated instances. Meta-learning has been widely adopted for such a …

Semantic relation extraction: a review of approaches, datasets, and evaluation methods with looking at the methods and datasets in the Persian language

H Gharagozlou, J Mohammadzadeh… - ACM Transactions on …, 2023 - dl.acm.org
A large volume of unstructured data, especially text data, is generated and exchanged daily.
Consequently, the importance of extracting patterns and discovering knowledge from textual …

A novel pipelined end-to-end relation extraction framework with entity mentions and contextual semantic representation

Z Liu, H Li, H Wang, Y Liao, X Liu, G Wu - Expert Systems with Applications, 2023 - Elsevier
The mainstream method of end-to-end relation extraction is to jointly extract entities and
relations by sharing span representation, which, however, may cause feature conflict. The …