Monolingual NER Spanish

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.

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.

Task results

System MacroF1
NLPeople 0.7276
PAI 0.7167
BizNER 0.7148
DAMO-NLP 0.8978
garNER 0.6373
CAIR-NLP 0.8363
D2KLab 0.6317
IXA/Cogcomp 0.7381
Sakura 0.7285

If you have published a result better than those on the list, send a message to indicating the result and the DOI of the article, along with a copy of it if it is not published openly.