A Cross-lingual Natural Language Processing Framework for Infodemic Management

The COVID-19 pandemic has put immense pressure on health systems which are further strained due to the misinformation surrounding it. Under such a situation, providing the right information at the right time is crucial. There is a growing demand for the management of information spread using Artific...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Pal, Ridam, Pandey, Rohan, Gautam, Vaibhav, Bhagat, Kanav, Sethi, Tavpritesh
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 30.10.2020
Subjects
Artificial intelligence
COVID-19
False information
Health care
Information management
Natural language processing
News
Words (language)
Online AccessGet full text

Cover

More Information
Summary:The COVID-19 pandemic has put immense pressure on health systems which are further strained due to the misinformation surrounding it. Under such a situation, providing the right information at the right time is crucial. There is a growing demand for the management of information spread using Artificial Intelligence. Hence, we have exploited the potential of Natural Language Processing for identifying relevant information that needs to be disseminated amongst the masses. In this work, we present a novel Cross-lingual Natural Language Processing framework to provide relevant information by matching daily news with trusted guidelines from the World Health Organization. The proposed pipeline deploys various techniques of NLP such as summarizers, word embeddings, and similarity metrics to provide users with news articles along with a corresponding healthcare guideline. A total of 36 models were evaluated and a combination of LexRank based summarizer on Word2Vec embedding with Word Mover distance metric outperformed all other models. This novel open-source approach can be used as a template for proactive dissemination of relevant healthcare information in the midst of misinformation spread associated with epidemics.
ISSN:2331-8422