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 / Amato, Alessandra; Amato, Flora; Barolli, Leonard; Bonavolonta', Francesco. - 312:(2022), pp. 49-58. [10.1007/978-3-030-84910-8_6]
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 | 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.
