A deep learning approach for aspect sentiment triplet extraction in portuguese and spanish.
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 tri...
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| Formato: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Brasil |
| Recursos: | Universidade de São Paulo (USP) |
| Repositorio: | Biblioteca Digital de Teses e Dissertações da USP |
| Idioma: | inglés |
| OAI Identifier: | oai:teses.usp.br:tde-08062022-081940 |
| Acesso em linha: | https://www.teses.usp.br/teses/disponiveis/3/3141/tde-08062022-081940/ |
| Access Level: | acceso abierto |
| Palavra-chave: | Aprendizado computacional Aspect sentiment triplet extraction Deep learning Inteligência artificial Natural language processing Processamento de linguagem natural |
| 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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