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Abstract
AMICA is an argument mining-based search engine, specifically designed for the analysis of scientific literature related to Covid-19. AMICA retrieves scientific papers based on matching keywords and ranks the results based on the papers’ argumentative content. An experimental evaluation conducted on a case study in collaboration with the Italian National Institute of Health shows that the AMICA ranking agrees with expert opinion, as well as, importantly, with the impartial quality criteria indicated by Cochrane Systematic Reviews.
Citation
Marco Lippi, Francesco Antici, Gianfranco Brambilla, Evaristo Cisbani, Andrea Galassi, Daniele Giansanti, Fabio Magurano, Antonella Rosi, Federico Ruggeri, and Paolo Torroni. AMICA: an argumentative search engine for COVID-19 literature. In Luc De Raedt, editor, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pages 5932–5935. ijcai.org, 2022.
@inproceedings{lippi-etal-2022-amica,
author = {Marco Lippi and
Francesco Antici and
Gianfranco Brambilla and
Evaristo Cisbani and
Andrea Galassi and
Daniele Giansanti and
Fabio Magurano and
Antonella Rosi and
Federico Ruggeri and
Paolo Torroni},
editor = {Luc De Raedt},
title = {{AMICA:} An Argumentative Search Engine for {COVID-19} Literature},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI} 2022, Vienna, Austria, 23-29 July
2022},
pages = {5932--5935},
publisher = {ijcai.org},
year = {2022},
url = {https://doi.org/10.24963/ijcai.2022/857},
doi = {10.24963/ijcai.2022/857},
timestamp = {Sun, 02 Oct 2022 16:08:01 +0200},
biburl = {https://dblp.org/rec/conf/ijcai/0001ABCGGMRRT22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
}