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AAAI 2024: "A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling"

P3M

Code for AAAI 2024 Conference paper [A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling].

Requirements

• Python (tested on 3.6.7)
• CUDA (tested on 11.0)
• PyTorch (tested on 1.7.1)
• Transformers (tested on 4.18.0)
• numpy (tested on 1.19.5)
• apex (tested on 0.1)
• opt-einsum (tested on 3.3.0)
• ujson
• tqdm

Dataset

The DocRED dataset can be downloaded following the instructions at link.

The Re-DocRED dataset can be downloaded following the instructions at link.

The ChemDisGene dataset can be downloaded following the instructions at link.

P3M
 |-- dataset
 |    |-- docred
 |    |    |-- train_annotated.json
 |    |    |-- train_distant.json
 |    |    |-- train_ext.json
 |    |    |-- train_revised.json
 |    |    |-- dev.json
 |    |    |-- dev_ext.json
 |    |    |-- dev_revised.json
 |    |    |-- test_revised.json
 |    |-- chemdisgene
 |    |    |-- train.json
 |    |    |-- valid.json
 |    |    |-- test.anno_all.json
 |-- meta
 |    |-- rel2id.json
 |    |-- relation_map.json

Training and Evaluation

DocRED

Train DocRED model with the following command:

>> sh scripts/run_bert_p3m.sh  # P3M BERT
>> sh scripts/run_roberta_p3m.sh  # P3M RoBERTa
>> sh scripts/run_bert_p3m_ext.sh  # P3M BERT Extremely unlabeled
>> sh scripts/run_roberta_p3m_ext.sh  # P3M RoBERTa Extremely unlabeled
>> sh scripts/run_bert_p3m_full.sh  # P3M BERT Fully supervised
>> sh scripts/run_roberta_p3m_full.sh  # P3M RoBERTa Fully supervised

ChemDisGene

Train ChemDisGene model with the following command:

>> sh scripts/run_bio_p3m.sh  # P3M PubmedBERT