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            <namePart type="given">Donia</namePart>
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            <namePart type="given">Chengqing</namePart>
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    <abstract>Emotion lexicons provide information about associations between words and emotions. They have proven useful in analyses of reviews, literary texts, and posts on social media, among other things. We evaluate the feasibility of deriving emotion lexicons cross-lingually, especially for low-resource languages, from existing emotion lexicons in resource-rich languages. For this, we start out from very small corpora to induce cross-lingually aligned vector spaces. Our study empirically analyses the effectiveness of the induced emotion lexicons by measuring translation precision and correlations with existing emotion lexicons, along with measurements on a downstream task of sentence emotion prediction.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings
%A Ramachandran, Arun
%A de Melo, Gerard
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F ramachandran-de-melo-2020-cross
%X Emotion lexicons provide information about associations between words and emotions. They have proven useful in analyses of reviews, literary texts, and posts on social media, among other things. We evaluate the feasibility of deriving emotion lexicons cross-lingually, especially for low-resource languages, from existing emotion lexicons in resource-rich languages. For this, we start out from very small corpora to induce cross-lingually aligned vector spaces. Our study empirically analyses the effectiveness of the induced emotion lexicons by measuring translation precision and correlations with existing emotion lexicons, along with measurements on a downstream task of sentence emotion prediction.
%R 10.18653/v1/2020.coling-main.517
%U https://aclanthology.org/2020.coling-main.517/
%U https://doi.org/10.18653/v1/2020.coling-main.517
%P 5879-5890

Markdown (Informal)

[Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings](https://aclanthology.org/2020.coling-main.517/) (Ramachandran & de Melo, COLING 2020)

• Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings (Ramachandran & de Melo, COLING 2020)

ACL

• Arun Ramachandran and Gerard de Melo. 2020. Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5879–5890, Barcelona, Spain (Online). International Committee on Computational Linguistics.