A deep learning approach for aspect sentiment triplet extraction in portuguese and spanish.

• Ano de defesa: 2022
• Autor(a) principal: Meléndez Barros, Jose de Jesus
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Aprendizado computacional; Aspect sentiment triplet extraction; Deep learning; Inteligência artificial; Natural language processing; Processamento de linguagem natural
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/3/3141/tde-08062022-081940/

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.