论文标题
利用伯特来改善基于方面的情感分析波斯语的表现
Exploiting BERT to improve aspect-based sentiment analysis performance on Persian language
论文作者
论文摘要
基于方面的情感分析（ABSA）是通过识别对文本中某个方面的意见极性的更详细的任务。由于它提供了更彻底和有用的信息，因此这种方法吸引了社区的更多关注。但是，关于波斯语的语言特定研究很少。本研究旨在改善波斯PARS-ABSA数据集上的ABSA。这项研究表明了使用预训练的BERT模型并利用在ABSA任务上使用句子对的可能性。结果表明，使用PARS-BERT预培训模型以及自然语言推断辅助句子（NLI-M）可以提高ABSA任务准确性高达91％，最高91％，高于PARS-ABSA数据集的最先进的研究。
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.
