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Applied Sciences
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AI Techniques in Computational and Automated Fact Checking
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AI Techniques in Computational and Automated Fact Checking

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 3399

Special Issue Editors

Prof. Dr. Giansalvatore Mecca

E-Mail Website
Guest Editor
Department of Mathematics, Computer Science and Economics, University of Basilicata, 85100 Potenza, Italy
Interests: data quality; data analytics; data integration; information extraction; database systems; string databases; deductive databases; object-oriented databases
Dr. Donatello Santoro

E-Mail Website
Guest Editor
Department of Mathematics, Computer Science, and Economics, Università della Basilicata, 85100 Potenza, Italy
Interests: data cleaning; schema mapping; data integration; entity resolution
Dr. Veltri Enzo

E-Mail Website
Guest Editor
Department of Mathematics, Computer Science, and Economics, Università della Basilicata, 85100 Potenza, Italy
Interests: databases; information systems; machine learning

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to Computational and Automated Fact Checking.

Today, with the spread of misinformation, automatically verifying textual claims against public data is crucial. Indeed, manual verification takes time and is expensive and fails to scale to a large volume of verified claims. AI, with the help of progress in natural language processing and natural language generation, will help toward advancements in textual understanding. Nevertheless, matching textual claims with tabular data is still challenging.

This Special Issue aims not only to collect the latest applications of AI techniques to computational and automated fact checking, but also to collect advancements in the field of information retrieval and query answering.

Prof. Dr. Giansalvatore Mecca
Dr. Donatello Santoro
Dr. Veltri Enzo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fact checking
  • computational fact checking
  • automatic fact checking
  • artificial intelligence
  • deep learning
  • NLP
  • information retrieval
  • query answering

Published Papers (2 papers)

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Research

15 pages, 334 KiB  
Article
Joint Extraction of Entities and Relations Based on Enhanced Span and Gate Mechanism
by Nan Zhang, Junfang Xin, Qiang Cai and Vera Chung
Appl. Sci. 2023, 13(19), 10643; https://doi.org/10.3390/app131910643 - 25 Sep 2023
Viewed by 744
Abstract
Although entity and relation joint extraction can obtain relational triples efficiently and accurately, there are a number of problems; for instance, the information between entity relations could be transferred better, entity extraction based on span is inefficient, and it is difficult to identify [...] Read more.
Although entity and relation joint extraction can obtain relational triples efficiently and accurately, there are a number of problems; for instance, the information between entity relations could be transferred better, entity extraction based on span is inefficient, and it is difficult to identify nested entities. In this paper, a joint entity and relation extraction model based on an Enhanced Span and Gate Mechanism (ESGM) is proposed to solve the above problems. We design a new span device to solve the problem of entity nesting and inefficiency. We use the pointer network method to predict the beginning and end of the span, and combine them through the one-to-many matching principle. A binary classification model is then trained to predict whether the span of the combination is the subject. In the object prediction stage, a gating unit is added to fuse the subject information with the sentence information and strengthen the information transfer between the entity and the relationship. Finally, the relationship is used as the mapping function to predict the tail entity related to the head entity. Our experimental results prove the effectiveness of this model. The precision of the proposed model reached 93.8% on the NYT dataset, which was 0.4% higher than that of the comparison model. Moreover, when the same experiment was conducted in a nested entity scenario, the accuracy of the proposed model was 4.4% higher than that of the comparison model. Full article
(This article belongs to the Special Issue AI Techniques in Computational and Automated Fact Checking)
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21 pages, 1233 KiB  
Article
PEINet: Joint Prompt and Evidence Inference Network via Language Family Policy for Zero-Shot Multilingual Fact Checking
by Xiaoyu Li, Weihong Wang, Jifei Fang, Li Jin, Hankun Kang and Chunbo Liu
Appl. Sci. 2022, 12(19), 9688; https://doi.org/10.3390/app12199688 - 27 Sep 2022
Cited by 2 | Viewed by 1796
Abstract
Zero-shot multilingual fact-checking, which aims to discover and infer subtle clues from the retrieved relevant evidence to verify the given claim in cross-language and cross-domain scenarios, is crucial for optimizing a free, trusted, wholesome global network environment. Previous works have made enlightening and [...] Read more.
Zero-shot multilingual fact-checking, which aims to discover and infer subtle clues from the retrieved relevant evidence to verify the given claim in cross-language and cross-domain scenarios, is crucial for optimizing a free, trusted, wholesome global network environment. Previous works have made enlightening and practical explorations in claim verification, while the zero-shot multilingual task faces new challenging gap issues: neglecting authenticity-dependent learning between multilingual claims, lacking heuristic checking, and a bottleneck of insufficient evidence. To alleviate these gaps, a novel Joint Prompt and Evidence Inference Network (PEINet) is proposed to verify the multilingual claim according to the human fact-checking cognitive paradigm. In detail, firstly, we leverage the language family encoding mechanism to strengthen knowledge transfer among multi-language claims. Then, the prompt turning module is designed to infer the falsity of the fact, and further, sufficient fine-grained evidence is extracted and aggregated based on a recursive graph attention network to verify the claim again. Finally, we build a unified inference framework via multi-task learning for final fact verification. The newly achieved state-of-the-art performance on the released challenging benchmark dataset that includes not only an out-of-domain test, but also a zero-shot test, proves the effectiveness of our framework, and further analysis demonstrates the superiority of our PEINet in multilingual claim verification and inference, especially in the zero-shot scenario. Full article
(This article belongs to the Special Issue AI Techniques in Computational and Automated Fact Checking)
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