We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that
covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as …

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 …

Incremental Named Entity Recognition (INER) involves the sequential learning of new entity
types without accessing the training data of previously learned types. However, INER faces …

Incremental Named Entity Recognition (INER) aims to continually train a model with new
data, recognizing emerging entity types without forgetting previously learned ones. Prior …

This paper describes the system developed by the USTC-NELSLIP team for SemEval-2022
Task 11 Multilingual Complex Named Entity Recognition (MultiCoNER). We propose a …

Named entity recognition (NER) in a real-world setting remains challenging and is impacted
by factors like text genre, corpus quality, and data availability. NER models trained on …

To address the scarcity of massive labeled data, cross-domain named entity recognition
(cross-domain NER) attracts increasing attention. Recent studies focus on decomposing …

Recently, Multi-modal Named Entity Recognition (MNER) has attracted a lot of attention.
Most of the work utilizes image information through region-level visual representations …

Cross-domain Named Entity Recognition (NER) aims to transfer knowledge from the source
domain to the target, alleviating expensive labeling costs in the target domain. Most prior …

Named Entity Recognition (NER) serves as a fundamental task in natural language
understanding, bearing direct implications for web content analysis, search engines, and …