Unsupervised Keyword Combination Query Generation from Online Health Related Content for Evidence-Based Fact Checking
23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021
; : 267-277, 2021.
Article
in English
| Scopus | ID: covidwho-1631618
ABSTRACT
False information in the domain of online health related articles is of great concern, which can be witnessed in the current pandemic situation of Covid-19. It is markedly different from fake news in the political context as health information should be evaluated against the most recent and reliable medical resources such as scholarly repositories. However, one of the challenges with such an approach is the retrieval of the pertinent resources. In this work, we formulate a new unsupervised task of generating queries using keywords extracted from a health-related article which can be further applied to retrieve relevant authoritative and reliable medical content from scholarly repositories to assess the article's veracity. We propose a three-step approach for it and illustrate that our method is able to generate effective queries. We also curate a new dataset to aid the evaluation for this task which will be made available upon request. © 2021 ACM.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021
Year:
2021
Document Type:
Article
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