Named Entity Recognition
918 papers with code • 6 benchmarks • 13 datasets
Benchmarks
These leaderboards are used to track progress in Named Entity Recognition
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Libraries
Use these libraries to find Named Entity Recognition models and implementations
CoNLL 2003
SciERC
Few-NERD
Most implemented papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
google-research/bert
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NAACL 2019
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Neural Architectures for Named Entity Recognition
State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available.
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
guillaumegenthial/sequence_tagging
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ACL 2016
State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
dmis-lab/biobert
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Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
ERNIE: Enhanced Representation through Knowledge Integration
We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration).
Named Entity Recognition with Bidirectional LSTM-CNNs
Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
studio-ousia/luke
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EMNLP 2020
In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.
A Unified MRC Framework for Named Entity Recognition
ShannonAI/mrc-for-flat-nested-ner
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ACL 2020
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
CamemBERT: a Tasty French Language Model
huggingface/transformers
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ACL 2020
We show that the use of web crawled data is preferable to the use of Wikipedia data.
