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SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
Active learning is an important technique for low-resource sequence labeling tasks. However, current active sequence labeling methods use the queried samples alone in each... -
OntoNotes 5.02
OntoNotes 5.02 -
MetaAug4NER
Self-augmentation for named entity recognition with meta reweighting -
MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recog...
The dataset used for SemEval-2022 Task 11 on identifying complex named entities in multilingual languages. -
SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER)
The dataset used for SemEval-2022 Task 11 on identifying complex named entities in Bangla. -
Attentive Neural Network for Named Entity Recognition in Vietnamese
Named Entity Recognition for Vietnamese using Attentive Neural Network -
Vietnamese Speech Dataset for Named Entity Recognition
The first Vietnamese speech dataset for NER task, and the first pre-trained public large-scale monolingual language model for Vietnamese that achieved the new state-of-the-art... -
Word representations
Word representations: A simple and general method for semi-supervised learning. -
CoNLL shared tasks
CoNLL shared tasks focused on language independent machine learning approaches for 4 entity types: person, location, organization and miscellaneous entities. -
Named Entity Recognition and Classification (NERC) task
Named Entity Recognition and Classification (NERC) task was first defined for the Sixth Message Understanding Conference (MUC 6) -
Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Fea...
Multilingual Named Entity Recognition approach based on a robust and general set of features across languages and datasets. -
CoNLL 2003
The CoNLL 2003 dataset contains 1393 articles with about 34K mentions, and the standard performance metric is mention-averaged accuracy.
