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- W4221154112 abstract "Acronym extraction is the task of identifying acronyms and their expanded forms in texts that is necessary for various NLP applications. Despite major progress for this task in recent years, one limitation of existing AE research is that they are limited to the English language and certain domains (i.e., scientific and biomedical). As such, challenges of AE in other languages and domains is mainly unexplored. Lacking annotated datasets in multiple languages and domains has been a major issue to hinder research in this area. To address this limitation, we propose a new dataset for multilingual multi-domain AE. Specifically, 27,200 sentences in 6 typologically different languages and 2 domains, i.e., Legal and Scientific, is manually annotated for AE. Our extensive experiments on the proposed dataset show that AE in different languages and different learning settings has unique challenges, emphasizing the necessity of further research on multilingual and multi-domain AE." @default.
- W4221154112 created "2022-04-03" @default.
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- W4221154112 date "2022-02-19" @default.
- W4221154112 modified "2023-09-23" @default.
- W4221154112 title "MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction" @default.
- W4221154112 doi "https://doi.org/10.48550/arxiv.2202.09694" @default.
- W4221154112 hasPublicationYear "2022" @default.
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