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 multilingual complex named entity recognition task of SemEval2020 required
participants to detect semantically ambiguous and complex entities in 11 languages. In
order to participate in this competition, a deep learning model is being used with the T5 text-
to-text language model and its multilingual version, MT5, along with the transformer's
encoder module. The subtoken check has also been introduced, resulting in a 4% increase
in the model F1-score in English. We also examined the use of the BPEmb model for …