A probabilistic polarity scoring method for Latent Semantic Scaling uses masked language models to compute seed-word occurrence probabilities, claimed to outperform spatial models in accuracy, interpretability, and consistency.
This evaluation results in sets of scores in 3,100 conditions in total
1 Pith paper cite this work. Polarity classification is still indexing.
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A New Semisupervised Technique for Polarity Analysis using Masked Language Models
A probabilistic polarity scoring method for Latent Semantic Scaling uses masked language models to compute seed-word occurrence probabilities, claimed to outperform spatial models in accuracy, interpretability, and consistency.