Due to the current emergency situation, caused by COVID-19, the scientific literature on the topic has rapidly grown. At the same time, purposeful and targeted research plans with strong background knowledge is urgently needed. However, the huge number of documents produced by multiple communities generates a fragmented terminology that may cause confusion in information retrieval. To this aim, in a comparative study, we test different techniques to efficiently cluster these publications for improving their level of findability.

A Comparative Study of Clustering Techniques Applied on Covid-19 Scientific Literature / V. Bellandi, P. Ceravolo, S. Maghool, S. Siccardi - In: 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS)[s.l] : IEEE, 2020. - ISBN 9780738124605. - pp. 1-8 [10.1109/IOTSMS52051.2020.9340213]

A Comparative Study of Clustering Techniques Applied on Covid-19 Scientific Literature

V. Bellandi
;
P. Ceravolo
;
S. Maghool
;
S. Siccardi
2020

Abstract

Due to the current emergency situation, caused by COVID-19, the scientific literature on the topic has rapidly grown. At the same time, purposeful and targeted research plans with strong background knowledge is urgently needed. However, the huge number of documents produced by multiple communities generates a fragmented terminology that may cause confusion in information retrieval. To this aim, in a comparative study, we test different techniques to efficiently cluster these publications for improving their level of findability.
Text Embedding; Document Clustering; COVID 19; Machine Learning
Settore INF/01 - Informatica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/822259
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