SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER)

Shervin Malmasi, Anjie Fang, Besnik Fetahu, Sudipta Kar, Oleg Rokhlenko


Abstract
We present the findings of SemEval-2022 Task 11 on Multilingual Complex Named Entity Recognition MULTICONER. Divided into 13 tracks, the task focused on methods to identify complex named entities (like names of movies, products and groups) in 11 languages in both monolingual and multi-lingual scenarios. Eleven tracks required building monolingual NER models for individual languages, one track focused on multilingual models able to work on all languages, and the last track featured code-mixed texts within any of these languages. The task is based on the MULTICONER dataset comprising of 2.3 millions instances in Bangla, Chinese, Dutch, English, Farsi, German, Hindi, Korean, Russian, Spanish, and Turkish. Results showed that methods fusing external knowledge into transformer models achieved the best results. However, identifying entities like creative works is still challenging even with external knowledge. MULTICONER was one of the most popular tasks in SemEval-2022 and it attracted 377 participants during the practice phase. 236 participants signed up for the final test phase and 55 teams submitted their systems.
Anthology ID:
2022.semeval-1.196
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1412–1437
Language:
URL:
https://aclanthology.org/2022.semeval-1.196
DOI:
10.18653/v1/2022.semeval-1.196
Bibkey:
Cite (ACL):
Shervin Malmasi, Anjie Fang, Besnik Fetahu, Sudipta Kar, and Oleg Rokhlenko. 2022. SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER). In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1412–1437, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER) (Malmasi et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.196.pdf
Data
MultiCoNERWNUT 2017