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2020
- 20.500.13089/1di7
Ce document est lié à :
https://hdl.handle.net/20.500.13089/1cho
Ce document est lié à :
https://doi.org/10.4000/books.aaccademia
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/isbn/979-12-80136-32-9
info:eu-repo/semantics/openAccess , https://creativecommons.org/licenses/by-nc-nd/4.0/
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Methods of analysis Analysis and chemistry Analytical methods Chemical analysis Analysis methods Analysis and examinationCiter ce document
Emanuele Di Rosa et al., « App2Check @ ATE_ABSITA 2020: Aspect Term Extraction and Aspect-based Sentiment Analysis », Accademia University Press
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Résumé
In this paper we describe and present the results of the system we specifically developed and submitted for our participation to the ATE_ABSITA 2020 evaluation campaign on the Aspect Term Extraction (ATE), Aspect-based Sentiment Analysis (ABSA), and Sentiment Analysis (SA) tasks. The official results show that App2Check ranks first in all of the three tasks, reaching a F1 score which is 0.14236 higher than the second best system in the ATE task and 0.11943 higher in the ABSA task; it shows a Root-Mean-Square Error (RMSE) that is 0.13075 lower than the second classified in the SA task.
