Large Language Models (LLMs) combined with instruction tuning have made significant
progress when generalizing to unseen tasks. However, they have been less successful in …

Large language models (LLMs) have shown impressive abilities for open-domain NLP
tasks. However, LLMs are sometimes too footloose for natural language understanding …

Traditional NER systems are typically trained to recognize coarse-grained entities, and less
attention is given to classifying entities into a hierarchy of fine-grained lower-level subtypes …

The rapid development of Large Language Models (LLMs) demonstrates remarkable
multilingual capabilities in natural language processing, attracting global attention in both …

We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering
33 entity classes across 12 languages, in both monolingual and multilingual settings. This …

Named entities–typically expressed via proper nouns–play a key role in Natural Language
Processing, as their identification and comprehension are crucial in tasks such as Relation …

The MultiCoNER\RNum {2} shared task aims to tackle multilingual named entity recognition
(NER) in fine-grained and noisy scenarios, and it inherits the semantic ambiguity and low …

Extraction of concepts and entities of interest from non-formal texts such as social media
posts and informal communication is an important capability for decision support systems in …

The MultiCoNER II shared task aims at detecting complex, ambiguous named entities with
fine-grained types in a low context setting. Previous winning systems incorporated external …

Named Entity Recognition (NER) is a key information extraction task with a long-standing
tradition. While recent studies address and aim to correct annotation errors via re-labeling …