A BERT-XGBoost hybrid is claimed to classify athletes' psychological states with 94% accuracy, but the result cannot be checked because the dataset, code, and evaluation details are absent.
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis
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abstract
In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works. Besides, we also standardize the comparative study by consistently utilizing a hold-out validation dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA.
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Real-Time Prediction for Athletes' Psychological States Using BERT-XGBoost: Enhancing Human-Computer Interaction
A BERT-XGBoost hybrid is claimed to classify athletes' psychological states with 94% accuracy, but the result cannot be checked because the dataset, code, and evaluation details are absent.