Information retrieval and extraction on COVID-19 clinical articles using graph community detection and Bio-BERT embeddings
1st Workshop on NLP for COVID-19 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
; 2020.
Article
in English
| Scopus | ID: covidwho-2256286
ABSTRACT
In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19. We build a similarity network on the articles where similarity is determined via shared citations and biological domain-specific sentence embeddings. Ego-splitting community detection on the article network is employed to cluster the articles and then the queries are matched with the clusters. Extractive summarization using BERT and PageRank methods is used to provide responses to the query. We also provide a Question-Answer bot on a small set of intents to demonstrate the efficacy of our model for an information extraction module. © ACL 2020.All right reserved.
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
1st Workshop on NLP for COVID-19 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Year:
2020
Document Type:
Article
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