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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| Challenge: | Existing lexicons of affect only show coarse associations, but are not accurate as human-created ones. |
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| Challenge: | Emotion lexicons describe the affective meaning of words but are limited in coverage for most languages. |
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| Challenge: | Emojis are increasingly used to convey affect, but their use is not trivial. |
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| Challenge: | Affective tasks such as sentiment analysis, emotion classification and sarcasm detection have enjoyed great popularity in recent years. |
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| Challenge: | Existing representational frameworks for emotion encoding are incompatible with semantic polarity, resulting in a large amount of incompatible emotion lexicons. |
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| Challenge: | Inappropriate and incorrect use of emotion lexicons can lead to harmful inferences . |
| Approach: | They propose to present some of the practical and ethical considerations involved in the creation and use of emotion lexicons. |
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| Challenge: | Using a synthetic sports feedback dataset, we evaluate open-weight LLMs’ ability to extract aspect-polarity pairs. |
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