Evaluating Methods for Extraction of Aspect Terms in Opinion Texts in Portuguese - the Challenges of Implicit Aspects

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Machado, Mateus Tarcinalli ; Pardo, Thiago Alexandre Salgueiro ; Mariani, J ; Calzolari, N ; Bechet, F ; Blache, P ; Choukri, K ; Cieri, C ; Declerck, T ; Goggi, S ; Isahara, H ; Maegaard, B ; Mazo, H ; Odijk, H ; Piperidis, S

• Tipo de documento: Artigo Científico
• Fonte: LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION; v. N/A, p. 10-pg., 2022-01-01.

Resumo

One of the challenges of aspect-based sentiment analysis is the implicit mention of aspects. These are more difficult to identify and may require world knowledge to do so. In this work, we evaluate frequency-based, hybrid, and machine learning methods, including the use of the pre-trained BERT language model, in the task of extracting aspect terms in opinionated texts in Portuguese, emphasizing the analysis of implicit aspects. Besides the comparative evaluation of methods, the differential of this work lies in the analysis's novelty using a typology of implicit aspects that shows the knowledge needed to identify each implicit aspect term, thus allowing a mapping of the strengths and weaknesses of each method. (AU)

• Processo FAPESP: 19/07665-4 - Centro de Inteligência Artificial
• Beneficiário: Fabio Gagliardi Cozman
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa em Engenharia