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We propose a novel hierarchy-independent "Do-Redo" model for nested NER. The proposed model overcomes a number of shortcomings in state-of-the-art techniques, including the detection of overlapping entities, slow inference speed, restrictions on length or number of predicted entities, and dependence on the number of nesting levels.

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dbagal/do_redo

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dataloaders

dataloaders

 
 

datasets

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do_redo_tokenizers

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evaluation-results

evaluation-results

 
 

logs

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.gitignore

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config.conf

config.conf

 
 

crf.py

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drug.pt

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genia.pt

genia.pt

 
 

gitignore.txt

gitignore.txt

 
 

model.py

model.py

 
 

test.py

test.py

 
 

train.py

train.py

 
 

utils.py

utils.py

 
 

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We propose a novel hierarchy-independent "Do-Redo" model for nested NER. The proposed model overcomes a number of shortcomings in state-of-the-art techniques, including the detection of overlapping entities, slow inference speed, restrictions on length or number of predicted entities, and dependence on the number of nesting levels.

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nlp natural-language-processing information-extraction named-entity-recognition nested-named-entity-recognition
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