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        <title>Non-Compositionality in Sentiment: New Data and Analyses</title>
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            <namePart type="given">Juan</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>When natural language phrases are combined, their meaning is often more than the sum of their parts. In the context of NLP tasks such as sentiment analysis, where the meaning of a phrase is its sentiment, that still applies. Many NLP studies on sentiment analysis, however, focus on the fact that sentiment computations are largely compositional. We, instead, set out to obtain non-compositionality ratings for phrases with respect to their sentiment. Our contributions are as follows: a) a methodology for obtaining those non-compositionality ratings, b) a resource of ratings for 259 phrases – NonCompSST – along with an analysis of that resource, and c) an evaluation of computational models for sentiment analysis using this new resource.</abstract>
    <identifier type="citekey">dankers-lucas-2023-non</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.342</identifier>
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%0 Conference Proceedings
%T Non-Compositionality in Sentiment: New Data and Analyses
%A Dankers, Verna
%A Lucas, Christopher
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F dankers-lucas-2023-non
%X When natural language phrases are combined, their meaning is often more than the sum of their parts. In the context of NLP tasks such as sentiment analysis, where the meaning of a phrase is its sentiment, that still applies. Many NLP studies on sentiment analysis, however, focus on the fact that sentiment computations are largely compositional. We, instead, set out to obtain non-compositionality ratings for phrases with respect to their sentiment. Our contributions are as follows: a) a methodology for obtaining those non-compositionality ratings, b) a resource of ratings for 259 phrases – NonCompSST – along with an analysis of that resource, and c) an evaluation of computational models for sentiment analysis using this new resource.
%R 10.18653/v1/2023.findings-emnlp.342
%U https://aclanthology.org/2023.findings-emnlp.342
%U https://doi.org/10.18653/v1/2023.findings-emnlp.342
%P 5150-5162

Markdown (Informal)

[Non-Compositionality in Sentiment: New Data and Analyses](https://aclanthology.org/2023.findings-emnlp.342) (Dankers & Lucas, Findings 2023)

• Non-Compositionality in Sentiment: New Data and Analyses (Dankers & Lucas, Findings 2023)

ACL

• Verna Dankers and Christopher Lucas. 2023. Non-Compositionality in Sentiment: New Data and Analyses. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5150–5162, Singapore. Association for Computational Linguistics.