This paper investigates data representation and extraction procedures for the management of domain-specific information regarding COVID-19 information. To integrate among different data sources, including data contained in COVID-19 related clinical texts written in natural language, Natural Language Processing (NLP) techniques and the main tools available for this purpose were studied. In particular, we use an NLP pipeline implemented in python to extract relevant information taken from COVID-19 related literature and apply lexicometric measures on it. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Automatic Measurement of Acquisition for COVID-19 Related Information

Flora Amato;Leonard Barolli;Francesco Bonavolontà
2022

Abstract

This paper investigates data representation and extraction procedures for the management of domain-specific information regarding COVID-19 information. To integrate among different data sources, including data contained in COVID-19 related clinical texts written in natural language, Natural Language Processing (NLP) techniques and the main tools available for this purpose were studied. In particular, we use an NLP pipeline implemented in python to extract relevant information taken from COVID-19 related literature and apply lexicometric measures on it. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
File in questo prodotto:
File Dimensione Formato  
_2021_06_INCoS__Amato.pdf

solo utenti autorizzati

Tipologia: Versione Editoriale (PDF)
Licenza: Accesso privato/ristretto
Dimensione 83.17 kB
Formato Adobe PDF
83.17 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/880472
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact