Public health fact verification based on semantic hierarchy graph

Yitian Sun; Guiyun Zhang

doi:10.1117/12.3033405

29 July 2024 Public health fact verification based on semantic hierarchy graph

Yitian Sun, Guiyun Zhang

Proceedings Volume 13214, Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024); 1321403 (2024) https://doi.org/10.1117/12.3033405
Event: Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024), 2024, Guangzhou, China

Abstract

The task of fact verification is to evaluate the truth of a claim-focused on plausible evidence, verifying the truth of a claim in public health is challenging because the need for reasoning across various retrievable pieces of evidence. Most fact-checking studies typically focus only on political claims, while few studies address fact verification on other topics. Therefore, this paper chooses to study fact verification in public health. To support this case study, this paper employs PUBHEALTH, a dataset containing claims related to public health. In this study, this paper presents an approach based on a semantic hierarchy. Different from most methods, which represent evidence sentences by concatenating or merging the characteristics of isolated evidence sentences, our method obtains the complex semantic hierarchy of evidence sentences through semantic analysis. Specifically, based on the XLNet model, the graph structure is initially utilized to redefine the relative distance of words. Then, a graph attention network and multi-layer graph convolutional network are used to propagate and aggregate information from neighboring nodes on the graph. Experiments on the PUBHEALTH dataset show that the proposed method achieves 76.2% in F1 score, which is substantially higher than the current highest score, indicating the effectiveness of the fact verification task based on semantic hierarchy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yitian Sun and Guiyun Zhang "Public health fact verification based on semantic hierarchy graph", Proc. SPIE 13214, Fourth International Conference on Digital Signal and Computer Communications (DSCC 2024), 1321403 (29 July 2024); https://doi.org/10.1117/12.3033405

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Semantics

Education and training

Matrices

Transformers

Ablation

Data modeling

Data processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years