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"Joint entity recognition and relation extraction as a multi-head selection problem" (Expert Syst. Appl, 2018)

paper

official tensorflow version

This model is extreamly useful for real-world RE usage. I originally reimplemented for a competition (Chinese IE). I will add CoNLL04 dataset and BERT model.

Requirement

  • python 3.7
  • pytorch 1.10

Dataset

Chinese IE

Chinese Information Extraction Competition link

Unzip *.json into ./raw_data/chinese/

CoNLL04

We use the data processed by official version.

already in ./raw_data/CoNLL04/

Run

python main.py --mode preprocessing --exp_name chinese_selection_re
python main.py --mode train --exp_name chinese_selection_re 
python main.py --mode evaluation --exp_name chinese_selection_re

If you want to try other experiments:

set exp_name as conll_selection_re or conll_bert_re

Result

Chinese

Training speed: 10min/epoch

precision recall f1
Ours (dev) 0.7443 0.6960 0.7194
Winner (test) 0.8975 0.8886 0.893

CoNLL04

Test set:

precision recall f1
Ours (LSTM) 0.6531 0.3153 0.4252
Ours (BERT-freeze) 0.5233 0.4975 0.5101
Official 0.6375 0.6043 0.6204

We use the strictest setting: a triplet is correct only if the relation and all the tokens of head and tail are correct.

Details

The model was originally used for Chinese IE, thus, it's a bit different from the official paper:

They use pretrained char-word embedding while we use word embedding initialized randomly; they use 3-layer LSTM while we use 1-layer LSTM.

TODO

  • Tune the hyperparameters for CoNLL04