DocOIE: A Document-level Context-Aware Dataset for OpenIE

DocOIE: Document-level context-aware Open Information Extraction

Introduction

DocOIE is a document-level context-aware dataset for Open Information Extraction. DocIE is a context-aware Open Information Extraction Model. The details of this work are elaborated in our paper published in Findings of ACL 2021.

DocOIE dataset

DocOIE consists of evaluation and training dataset.

Evaluation dataset \DocOIE\data_evaluation\ contains 800 expert-annotated sentences, sampled from 80 documents in two domains (healthcare and transportation).

Training dataset \DocOIE\data_train\ contains 2,400 documents from the two domains (healthcare and transportation); 1,200 documents in each domain. All sentences from these documents are used to bootstrap pseudo labels for neural model training.
Note: Only document IDs are included in DocOIE Training dataset, for document collection at PatFT.

DocIE model

Installation Instructions

Credits given to IMoJIE, we build our DocIE model based on it.

Use a python-3.7 environment and install the dependencies using,

pip install -r requirements.txt

This will install custom versions of allennlp and pytorch_transformers based on the code in the folder.

Training dataset prepration

Collect training dataset according to document IDs. Use ReVerb and OpenIE4 to extract pseudo labels for model training. Place the training files to

• data/train/healthcare/train_extractions_reverb_oie4.json for healthcare domain
• data/train/transport/train_extractions_oie4_reverb.json for transportation domain

Running the code

python allennlp_script.py --param_path DocIE/configs/docie_healthcare.json --s trained_models/doc_healthcare --beam_size 3 --max_length 500 --max_sent_length 40 --context_window 5 --top_encoder true

Arguments:

• param_path: file containing all the parameters for the model
• s: path of the directory where the model will be saved
• context_window: window size of the surrounding sentences
• max_length: maximum length of source sentence and surrounding sentences (note that the max length for BERT is 512).
• max_sent_length: maximum length of each surrounding sentence.
• top_encoder: whether or not to use additonal transformer layers as top enconder.

Citing

If you use this code in your research, please cite:

@inproceedings{dong-etal-2021-docoie,
    title = "{D}oc{OIE}: A Document-level Context-Aware Dataset for {O}pen{IE}",
    author = "Dong, Kuicai  and
      Yilin, Zhao  and
      Sun, Aixin  and
      Kim, Jung-Jae  and
      Li, Xiaoli",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.210",
    doi = "10.18653/v1/2021.findings-acl.210",
    pages = "2377--2389",
}

Contact

In case of any issues, please send a mail to kuicai001 (at) e (dot) ntu (dot) edu (dot) sg