Name
Title | ||
|---|---|---|
MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction. |
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). Challenges of AE in other languages and domains are mainly unexplored. As such, lacking annotated datasets in multiple languages and domains has been a major issue to prevent research in this direction. To address this limitation, we propose a new dataset for multilingual and multi-domain AE. Specifically, 27,200 sentences in 6 different languages and 2 new domains, i.e., legal and scientific, are manually annotated for AE. Our experiments on the dataset show that AE in different languages and learning settings has unique challenges, emphasizing the necessity of further research on multilingual and multi-domain AE. |
Year | Venue | DocType |
|---|---|---|
2022 | International Conference on Computational Linguistics | Conference |
Volume | Citations | PageRank |
Proceedings of the 29th International Conference on Computational Linguistics | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Pouran Ben Veyseh | 1 | 0 | 5.75 |
| Nicole Meister | 2 | 0 | 0.68 |
| Seunghyun Yoon | 3 | 0 | 0.34 |
| Rajiv Jain | 4 | 4 | 5.16 |
| Franck Dernoncourt | 5 | 149 | 35.39 |
| Thuy Thanh Nguyen | 6 | 236 | 32.55 |