SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)

Complex Named Entities, like the titles of creative works, are not simple nouns and pose challenges for NER systems. Divided into 13 tracks, the task focused on methods to identify complex fine-grained named entities (like WRITTENWORK, VEHICLE, MUSICALGRP ) across 12 languages, in both monolingual and multilingual scenarios, as well as noisy settings. 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.