Papers with estimating word-level emotion intensity scores

1 papers
Guilt by Association: Emotion Intensities in Lexical Representations (2021.emnlp-main)

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Challenge: linguistic models have a higher correlation with human ground truth ratings than labeled data . word vectors have often been evaluated on standard word relatedness benchmarks .
Approach: They propose to use unsupervised, supervised, and finally supervised methods to extract emotional associations from pretrained vectors and models.
Outcome: The proposed method shows higher correlation with ground truth ratings than state-of-the-art lexicons based on labeled data.

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