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            <namePart type="given">Marie-Francine</namePart>
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    <abstract>In this paper we study pejorative language, an under-explored topic in computational linguistics. Unlike existing models of offensive language and hate speech, pejorative language manifests itself primarily at the lexical level, and describes a word that is used with a negative connotation, making it different from offensive language or other more studied categories. Pejorativity is also context-dependent: the same word can be used with or without pejorative connotations, thus pejorativity detection is essentially a problem similar to word sense disambiguation. We leverage online dictionaries to build a multilingual lexicon of pejorative terms for English, Spanish, Italian, and Romanian. We additionally release a dataset of tweets annotated for pejorative use. Based on these resources, we present an analysis of the usage and occurrence of pejorative words in social media, and present an attempt to automatically disambiguate pejorative usage in our dataset.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.296</identifier>
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%0 Conference Proceedings
%T A Computational Exploration of Pejorative Language in Social Media
%A Dinu, Liviu P.
%A Iordache, Ioan-Bogdan
%A Uban, Ana Sabina
%A Zampieri, Marcos
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F dinu-etal-2021-computational-exploration
%X In this paper we study pejorative language, an under-explored topic in computational linguistics. Unlike existing models of offensive language and hate speech, pejorative language manifests itself primarily at the lexical level, and describes a word that is used with a negative connotation, making it different from offensive language or other more studied categories. Pejorativity is also context-dependent: the same word can be used with or without pejorative connotations, thus pejorativity detection is essentially a problem similar to word sense disambiguation. We leverage online dictionaries to build a multilingual lexicon of pejorative terms for English, Spanish, Italian, and Romanian. We additionally release a dataset of tweets annotated for pejorative use. Based on these resources, we present an analysis of the usage and occurrence of pejorative words in social media, and present an attempt to automatically disambiguate pejorative usage in our dataset.
%R 10.18653/v1/2021.findings-emnlp.296
%U https://aclanthology.org/2021.findings-emnlp.296
%U https://doi.org/10.18653/v1/2021.findings-emnlp.296
%P 3493-3498

Markdown (Informal)

[A Computational Exploration of Pejorative Language in Social Media](https://aclanthology.org/2021.findings-emnlp.296) (Dinu et al., Findings 2021)

• A Computational Exploration of Pejorative Language in Social Media (Dinu et al., Findings 2021)

ACL

• Liviu P. Dinu, Ioan-Bogdan Iordache, Ana Sabina Uban, and Marcos Zampieri. 2021. A Computational Exploration of Pejorative Language in Social Media. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3493–3498, Punta Cana, Dominican Republic. Association for Computational Linguistics.