Prompt tuning on Graph-Augmented Low-Resource text classification

Prompt tuning on Graph-Augmented Low-Resource text classification

Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with no or few labeled samples, presents a serio...

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Bibliographic Details
Main Authors: WEN, Zhihao, FANG, Yuan
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
Accuracy
graph
Ink
low-resource learning Oils
Paints
pre-training
prompt
Task analysis
Text categorization
Text classification
Tuning
Databases and Information Systems
Theory and Algorithms
Online Access:https://ink.library.smu.edu.sg/sis_research/9275
https://ink.library.smu.edu.sg/context/sis_research/article/10275/viewcontent/Prompt_tuning_GALR_tc_av.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/9275
https://ink.library.smu.edu.sg/context/sis_research/article/10275/viewcontent/Prompt_tuning_GALR_tc_av.pdf

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