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We present the findings of SemEval-2022 Task 11 on Multilingual Complex Named Entity Recognition MULTICONER. 1 Divided into 13 tracks, the task focused on methods to identify complex named entities (like media titles, products, and groups) in 11 languages in both monolingual and multi-lingual scenarios. Eleven tracks were for 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 used the MULTICONER dataset, composed of 2.3 million 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 performance. The largest gains were on the Creative Work and Group entity classes, which are 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. The final test phase had 236 participants, and 55 teams submitted their systems.
Malmasi et al. (Sat,) studied this question.