SGPT: Semantic Graphs based Pre-training for Aspect-based Sentiment Analysis
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Abstract (arXiv)
Previous studies show effective of pre-trained language models for sentiment analysis. However, most of these studies ignore the importance of sentimental information for pre-trained models.Therefore, we fully investigate the sentimental information for pre-trained models and enhance pre-trained language models with semantic graphs for sentiment analysis.In particular, we introduce Semantic Graphs based Pre-training(SGPT) using semantic graphs to obtain synonym knowledge for aspect-sentiment pairs and similar aspect/sentiment terms.We then optimize the pre-trained language model with the semantic graphs.Empirical studies on several downstream tasks show that proposed model outperforms strong pre-trained baselines. The results also show the effectiveness of proposed semantic graphs for pre-trained model.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 26 May 2021.
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5 more
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