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This is the code for An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition

We found previous nested NER related work used different sentence tokenizations, resulting in different number of sentences and entities, which would make the comparison between different papers unfair. To solve this issue, we propose using the pre-processing scripts under preprocess to get the ACE2004, ACE2005 and Genia datasets. Please refer the readme for more details.

To run the genia dataset, using

python train.py -n 5 --lr 7e-6 --cnn_dim 200 --biaffine_size 400 --n_head 4 -b 8 -d genia  --logit_drop 0 --cnn_depth 3

for ACE2004, using

python train.py -n 50 --lr 2e-5 --cnn_dim 120 --biaffine_size 200 --n_head 5 -b 48 -d ace2004 --logit_drop 0.1 --cnn_depth 2

for ACE2005, using

python train.py -n 50 --lr 2e-5 --cnn_dim 120 --biaffine_size 200 --n_head 5 -b 48 -d ace2005 --logit_drop 0 --cnn_depth 2

Here, we set n_heads, cnn_dim and biaffine_size for small number of parameters, based on our experiment, reduce n_head and enlarge cnn_dim and biaffine_size should get slightly better performance.

Customized data

If you want to use your own data, please organize your data line like the following way, the data folder should have the following files

customized_data/
    - train.jsonelines
    - dev.jsonlines
    - test.jsonlines

in each file, each line should be a json object, like the following

{"tokens": ["Our", "data", "suggest", "that", "lipoxygenase", "metabolites", "activate", "ROI", "formation", "which", "then", "induce", "IL-2", "expression", "via", "NF-kappa", "B", "activation", "."], "entity_mentions": [{"entity_type": "protein", "start": 12, "end": 13, "text": "IL-2"}, {"entity_type": "protein", "start": 15, "end": 17, "text": "NF-kappa B"}, {"entity_type": "protein", "start": 4, "end": 5, "text": "lipoxygenase"}, {"entity_type": "protein", "start": 4, "end": 6, "text": "lipoxygenase metabolites"}]}

the entity start and end is inclusive and exclusive, respectively.

  • [update in 20220818]
    We add pre-processing code to extract Genia entities from raw data. We split train/dev/test based on documents to facilitate document-level NER study.