A deep learning approach for aspect sentiment triplet extraction in portuguese and spanish.
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da USP |
Texto Completo: | https://www.teses.usp.br/teses/disponiveis/3/3141/tde-08062022-081940/ |
Resumo: | Aspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA), which aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence Large rooms and great breakfast, ASTE outputs the triplet T = {(rooms, large, positive), (breakfast, great, positive)}. Although several approaches to ASBA have recently been proposed, those for Portuguese/Spanish have been mostly limited to extracting only aspects, without addressing ASTE tasks. This work aims to develop a framework based on Deep Learning to perform the Aspect Sentiment Triplet Extraction task in Portuguese and Spanish. The framework uses BERT as a context-awareness sentence encoder, multiple parallel non-linear layers to get aspect and opinion representations and a Graph Attention layer along with a Biaffine scorer to determine the sentiment dependency between each aspect-opinion pair. The comparison results show that our proposed framework significantly outperforms the baselines in Portuguese/Spanish and is competitive with its counterparts in English. |
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Biblioteca Digital de Teses e Dissertações da USP |
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A deep learning approach for aspect sentiment triplet extraction in portuguese and spanish.Uma abordagem de aprendizado profundo para extração de trigêmeos de sentimento de aspecto em português e espanhol.Aprendizado computacionalAspect sentiment triplet extractionDeep learningInteligência artificialNatural language processingProcessamento de linguagem naturalAspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA), which aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence Large rooms and great breakfast, ASTE outputs the triplet T = {(rooms, large, positive), (breakfast, great, positive)}. Although several approaches to ASBA have recently been proposed, those for Portuguese/Spanish have been mostly limited to extracting only aspects, without addressing ASTE tasks. This work aims to develop a framework based on Deep Learning to perform the Aspect Sentiment Triplet Extraction task in Portuguese and Spanish. The framework uses BERT as a context-awareness sentence encoder, multiple parallel non-linear layers to get aspect and opinion representations and a Graph Attention layer along with a Biaffine scorer to determine the sentiment dependency between each aspect-opinion pair. The comparison results show that our proposed framework significantly outperforms the baselines in Portuguese/Spanish and is competitive with its counterparts in English.Aspect Sentiment Triplet Extraction (ASTE) é uma subtarefa de Aspect-Based Sentiment Analysis (ABSA), que visa extrair pares de opiniões e aspectos de uma frase e identificar a polaridade de sentimento associada a eles. Por exemplo, dada a sentença Os quartos são amplos e ótimo café da manhã, ASTE gera o tripleto T = {(quartos, amplos, positive), (café de manhã, ótimo, positive)}. Embora várias abordagens tenham sido propostas recentemente, os trabalhos disponíveis em português e espanhol tem se limitado, em sua maioria, a extrair apenas aspectos, sem abordar tarefas ASTE. Este trabalho tem como objetivo desenvolver um framework baseado em Deep Learning para executar a tarefa de Aspect Sentiment Triplet Extraction em português e espanhol. O framework usa o BERT como um codificador de frase de consciência de contexto, várias camadas paralelas não lineares obtêm as representações dos aspectos e as opiniões e uma camada Graph Attention junto com um scorer não linear determina a dependência de sentimento entre cada par aspecto-opinião. Os resultados mostram que nosso framework proposto supera significativamente as linhas de base em português-espanhol e é competitiva com suas contrapartes em inglês.Biblioteca Digitais de Teses e Dissertações da USPBona, Glauber DeMeléndez Barros, Jose de Jesus 2022-02-17info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/3/3141/tde-08062022-081940/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2022-06-08T13:36:18Zoai:teses.usp.br:tde-08062022-081940Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212022-06-08T13:36:18Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
A deep learning approach for aspect sentiment triplet extraction in portuguese and spanish. Uma abordagem de aprendizado profundo para extração de trigêmeos de sentimento de aspecto em português e espanhol. |
title |
A deep learning approach for aspect sentiment triplet extraction in portuguese and spanish. |
spellingShingle |
A deep learning approach for aspect sentiment triplet extraction in portuguese and spanish. Meléndez Barros, Jose de Jesus Aprendizado computacional Aspect sentiment triplet extraction Deep learning Inteligência artificial Natural language processing Processamento de linguagem natural |
title_short |
A deep learning approach for aspect sentiment triplet extraction in portuguese and spanish. |
title_full |
A deep learning approach for aspect sentiment triplet extraction in portuguese and spanish. |
title_fullStr |
A deep learning approach for aspect sentiment triplet extraction in portuguese and spanish. |
title_full_unstemmed |
A deep learning approach for aspect sentiment triplet extraction in portuguese and spanish. |
title_sort |
A deep learning approach for aspect sentiment triplet extraction in portuguese and spanish. |
author |
Meléndez Barros, Jose de Jesus |
author_facet |
Meléndez Barros, Jose de Jesus |
author_role |
author |
dc.contributor.none.fl_str_mv |
Bona, Glauber De |
dc.contributor.author.fl_str_mv |
Meléndez Barros, Jose de Jesus |
dc.subject.por.fl_str_mv |
Aprendizado computacional Aspect sentiment triplet extraction Deep learning Inteligência artificial Natural language processing Processamento de linguagem natural |
topic |
Aprendizado computacional Aspect sentiment triplet extraction Deep learning Inteligência artificial Natural language processing Processamento de linguagem natural |
description |
Aspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA), which aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence Large rooms and great breakfast, ASTE outputs the triplet T = {(rooms, large, positive), (breakfast, great, positive)}. Although several approaches to ASBA have recently been proposed, those for Portuguese/Spanish have been mostly limited to extracting only aspects, without addressing ASTE tasks. This work aims to develop a framework based on Deep Learning to perform the Aspect Sentiment Triplet Extraction task in Portuguese and Spanish. The framework uses BERT as a context-awareness sentence encoder, multiple parallel non-linear layers to get aspect and opinion representations and a Graph Attention layer along with a Biaffine scorer to determine the sentiment dependency between each aspect-opinion pair. The comparison results show that our proposed framework significantly outperforms the baselines in Portuguese/Spanish and is competitive with its counterparts in English. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-02-17 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.teses.usp.br/teses/disponiveis/3/3141/tde-08062022-081940/ |
url |
https://www.teses.usp.br/teses/disponiveis/3/3141/tde-08062022-081940/ |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
|
dc.rights.driver.fl_str_mv |
Liberar o conteúdo para acesso público. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Liberar o conteúdo para acesso público. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.coverage.none.fl_str_mv |
|
dc.publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da USP instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Biblioteca Digital de Teses e Dissertações da USP |
collection |
Biblioteca Digital de Teses e Dissertações da USP |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br |
_version_ |
1809090513372971008 |