@inproceedings{pessutto-moreira-2022-ufrgsent,
title = "{UFRGS}ent at {S}em{E}val-2022 Task 10: Structured Sentiment Analysis using a Question Answering Model",
author = "Pessutto, Lucas and
Moreira, Viviane",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.189",
doi = "10.18653/v1/2022.semeval-1.189",
pages = "1360--1365",
abstract = "This paper describes the system submitted by our team (UFRGSent) to SemEval-2022 Task 10: Structured Sentiment Analysis. We propose a multilingual approach that relies on a Question Answering model to find tuples consisting of aspect, opinion, and holder. The approach starts from general questions and uses the extracted tuple elements to find the remaining components. Finally, we employ an aspect sentiment classification model to classify the polarity of the entire tuple. Despite our method being in a mid-rank position on SemEval competition, we show that the question-answering approach can achieve good coverage retrieving sentiment tuples, allowing room for improvements in the technique.",
}
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<abstract>This paper describes the system submitted by our team (UFRGSent) to SemEval-2022 Task 10: Structured Sentiment Analysis. We propose a multilingual approach that relies on a Question Answering model to find tuples consisting of aspect, opinion, and holder. The approach starts from general questions and uses the extracted tuple elements to find the remaining components. Finally, we employ an aspect sentiment classification model to classify the polarity of the entire tuple. Despite our method being in a mid-rank position on SemEval competition, we show that the question-answering approach can achieve good coverage retrieving sentiment tuples, allowing room for improvements in the technique.</abstract>
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%0 Conference Proceedings
%T UFRGSent at SemEval-2022 Task 10: Structured Sentiment Analysis using a Question Answering Model
%A Pessutto, Lucas
%A Moreira, Viviane
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F pessutto-moreira-2022-ufrgsent
%X This paper describes the system submitted by our team (UFRGSent) to SemEval-2022 Task 10: Structured Sentiment Analysis. We propose a multilingual approach that relies on a Question Answering model to find tuples consisting of aspect, opinion, and holder. The approach starts from general questions and uses the extracted tuple elements to find the remaining components. Finally, we employ an aspect sentiment classification model to classify the polarity of the entire tuple. Despite our method being in a mid-rank position on SemEval competition, we show that the question-answering approach can achieve good coverage retrieving sentiment tuples, allowing room for improvements in the technique.
%R 10.18653/v1/2022.semeval-1.189
%U https://aclanthology.org/2022.semeval-1.189
%U https://doi.org/10.18653/v1/2022.semeval-1.189
%P 1360-1365
Markdown (Informal)
[UFRGSent at SemEval-2022 Task 10: Structured Sentiment Analysis using a Question Answering Model](https://aclanthology.org/2022.semeval-1.189) (Pessutto & Moreira, SemEval 2022)
ACL