"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 3.6
• pytorch 1.20

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.

ADD

offline bert model fix bugs of bert tokenizer update and add all requirements

TODO

• Tune the hyperparameters of BERT+MUL for Chinese
• Add full fine-tune bert