Divided into 13 tracks, the MULTICONER competition focused on methods to identify complex fine-grained named entities across 12 languages, in both monolingual and multilingual scenarios, as well as noisy settings. The coarse grained types are: person, location, group, product, creative work and medical. The task used the MultiCoNER V2 dataset, composed of 2.2 million instances in Bangla, Chinese, English, Farsi, French, German, Hindi, Italian., Portuguese, Spanish, Swedish, and Ukrainian. This task is consists of entity recognition in Spanish.
Publication
Besnik Fetahu, Sudipta Kar, Zhiyu Chen, Oleg Rokhlenko, and Shervin Malmasi. 2023. SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2247–2265, Toronto, Canada. Association for Computational Linguistics.
Language
Spanish
English
URL Task
NLP topic
Abstract task
Dataset
Year
2023
Publication link
Task results
| System | MacroF1 Sort ascending |
|---|---|
| DAMO-NLP | 0.8978 |
| CAIR-NLP | 0.8363 |
| USTC-NELSLIP | 0.7444 |
| IXA/Cogcomp | 0.7381 |
| Sakura | 0.7285 |
| NLPeople | 0.7276 |
| PAI | 0.7167 |
| BizNER | 0.7148 |
| garNER | 0.6373 |
| D2KLab | 0.6317 |
