IJCAI 2022poster6 citations
AMICA: An Argumentative Search Engine for COVID-19 Literature
Marco Lippi, Francesco Antici, Gianfranco Brambilla, Evaristo Cisbani, Andrea Galassi, Daniele Giansanti, Fabio Magurano, Antonella Rosi
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.
Natural Language Processing: ApplicationsMultidisciplinary Topics and Applications: Health and MedicineNatural Language Processing: Information Retrieval and Text Mining
BibTeX
@inproceedings{ijcai2022p857,
title = {AMICA: An Argumentative Search Engine for COVID-19 Literature},
author = {Lippi, Marco and Antici, Francesco and Brambilla, Gianfranco and Cisbani, Evaristo and Galassi, Andrea and Giansanti, Daniele and Magurano, Fabio and Rosi, Antonella and Ruggeri, Federico and Torroni, Paolo},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {5932--5935},
year = {2022},
month = {7},
note = {Demo Track},
doi = {10.24963/ijcai.2022/857},
url = {https://doi.org/10.24963/ijcai.2022/857},
}