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Exploiting BERT to improve aspect-based sentiment analysis performance on Persian language

2020-12-02 16:47:20
H. Jafarian, A. H. Taghavi, A. Javaheri, R. Rawassizadeh
arXiv_CL
         
Abstract

Aspect-based sentiment analysis (ABSA) is a more detailed task in sentiment analysis, by identifying opinion polarity toward a certain aspect in a text. This method is attracting more attention from the community, due to the fact that it provides more thorough and useful information. However, there are few language-specific researches on Persian language. The present research aims to improve the ABSA on the Persian Pars-ABSA dataset. This research shows the potential of using pre-trained BERT model and taking advantage of using sentence-pair input on an ABSA task. The results indicate that employing Pars-BERT pre-trained model along with natural language inference auxiliary sentence (NLI-M) could boost the ABSA task accuracy up to 91% which is 5.5% (absolute) higher than state-of-the-art studies on Pars-ABSA dataset.

Abstract (translated)

URL

https://arxiv.org/abs/2012.07510

PDF

https://arxiv.org/pdf/2012.07510.pdf

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