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Keywords = aspect sentiment quad prediction

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14 pages, 2848 KB  
Article
Generative Aspect Sentiment Quad Prediction with Self-Inference Template
by Yashi Qin and Shu Lv
Appl. Sci. 2024, 14(14), 6017; https://doi.org/10.3390/app14146017 - 10 Jul 2024
Cited by 2 | Viewed by 1928
Abstract
Aspect Sentiment Quad Prediction is a research topic of paramount significance and complexity within the Aspect-Based Sentiment Analysis task. Leveraging the generative paradigm of the T5 model, we achieve end-to-end extraction of aspect sentiment elements by paraphrasing the original text into sentences predefined [...] Read more.
Aspect Sentiment Quad Prediction is a research topic of paramount significance and complexity within the Aspect-Based Sentiment Analysis task. Leveraging the generative paradigm of the T5 model, we achieve end-to-end extraction of aspect sentiment elements by paraphrasing the original text into sentences predefined by templates. Current research predominantly confines templates to single sentences or directly concatenates sentiment elements using a few symbols, limiting the model’s reasoning opportunities. In this work, we introduce a Self-Inference Template (SIT) to guide the model in thoughtful reasoning, facilitating a step-by-step inference generation process. This approach enables the model to more accurately identify aspect sentiment elements and their interdependencies. Experimental results demonstrate a significant improvement in quadruplet prediction performance under constant time costs, effectively mitigating overfitting issues caused by limited data volume to some extent. Full article
(This article belongs to the Special Issue AI Empowered Sentiment Analysis)
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