{"title":"App2Check @ ATE_ABSITA 2020: Aspect Term Extraction and Aspect-based Sentiment Analysis (short paper)","authors":"E. Rosa, A. Durante","doi":"10.4000/BOOKS.AACCADEMIA.6892","DOIUrl":null,"url":null,"abstract":"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-MeanSquare Error (RMSE) that is 0.13075 lower than the second classified in the SA","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4000/BOOKS.AACCADEMIA.6892","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
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-MeanSquare Error (RMSE) that is 0.13075 lower than the second classified in the SA
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