WebIE

This is a repository for the dataset of our paper WebIE: Faithful and Robust Information Extraction on the Web, published at ACL 2023.

Our dataset is created from the c4 dataset, last downloaded May 2023. We only release our annotations, alongside preprocessing scripts to extract the sentences from c4 that we used to create the annotations. Please note that the dataset generated using these scripts may differ slightly from the one we used in the paper.

Installation

First, activate a virtual environment, e.g. conda. Then, install the HuggingFace datasets library.

git clone https://github.com/amazon-science/webie.git
cd WebIE
conda create -n webie python=3.8
conda activate
pip install datasets

Data Format

The WebIE annotations can be found in data/webie_annotations and the mWebIE annotations can be found in data/mwebie_annotations.

Each example in the dataset follows the format:

{
    "id": # id of the example, 
    "uri": # url of the example, 
    "meta_obj": # metadata of the example (triples, nli scores, etc), 
    "output": # gold answer, 
    "timestamp": # url timestamp, 
    "span": {
        "start": # character start index in the url's doc, 
        "end": # character end index in the doc url's doc}
    }

To create the full dataset, download the c4 data, and filter to only contains the subset used in WebIE. This might take a few hours, and you might need a large cache (350GB) for this. You can use --cache_dir option to specify your cache directory. E.g.

cd sentence_extractor
python get_c4_subset.py --target_dir ../data/webie_c4 --cache_dir /cache/huggingface/datasets

Extract Sentences from C4

To extract the sentences, use the following command:

python extract_sentences.py --annotation_dir ../data/webie_annotations --data_dir ../data/webie_c4 --target_dir ../data/webie_complete

For mWebIE, you can add --multilingual flag:

python extract_sentences.py --annotation_dir ../data/mwebie_annotations --data_dir ../data/webie_c4 --target_dir ../data/mwebie_complete --multilingual

In the generated data, you will see a new json field input which is the input sentence for that example. Both input and output follow the same format used for the GenIE paper.

Human Annotations

The ids of the examples that are validated with human annotations can be found in data/annotated_ids.txt.

LICENSE

This project is licensed under the CC BY-NC-4.0 License. The c4 data may be subject to other licenses and copyrights, as applicable.

Citation

@inproceedings{whitehouse-etal-2023-webie,
    title = "{W}eb{IE}: Faithful and Robust Information Extraction on the Web",
    author = "Whitehouse, Chenxi and Vania, Clara and Aji, Alham Fikri and Christodoulopoulos, Christos and Pierleoni, Andrea",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.428",
    pages = "7734--7755",
}