DWIE (Deutsche Welle corpus for Information Extraction) is a new dataset for document-level multi-task Information Extraction (IE). It combines four main IE sub-tasks:
- Named Entity Recognition: 23,130 entities classified in 311 multi-label entity types (tags).
- Coreference Resolution: 43,373 entity mentions clustered in 23,130 entities.
- Relation Extraction: 21,749 annotated relations between entities classified in 65 multi-label relation types.
- Entity Linking: the named entities are linked to Wikipedia (version 20181115).
DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document. This contrasts with currently dominant mention-driven approaches that start from the detection and classification of named entity mentions in individual sentences. Also, the dataset was randomly sampled from a news platform (English online content from Deutsche Welle), and the annotation scheme was generated to cover that content. This makes the setting more realistic than in datasets with pre-determined annotation schemes, and non-uniform sampling of content to obtain balanced annotations.
We hope DWIE will help in promoting research in multi-task information extraction. We will be happy to discuss our work, and are open for collaboration in the future.
Publicly available DWIE annotations are located in the data/annos directory.
In order to get the content of each of the annotated articles, it is necessary
to run the following script:
pip install -r requirements_download_dataset.txt
python src/dwie_download.py
This script will retrieve the content of the articles using Deutsche Welle web service,
add it to the annotation files, and save it in data/annos_with_content directory.
It will also check that the hash representations of the downloaded articles match
the hash representations of the articles we use in our experiments
(please contact klim.zaporojets@ugent.be if the script outputs error messages).
Each of the annotated articles in data/annos_with_content is located in a different .json file
with the following keys:
id: unique identifier of the article.content: textual content of the article downloaded withsrc/dwie_download.pyscript.tags: used to differentiate betweentrainandtestsets of documents.mentions: a list of entity mentions in the article each with the following keys:begin: offset of the first character of the mention (insidecontentfield).end: offset of the last character of the mention (insidecontentfield).text: the textual representation of the entity mention.concept: the id of the entity that represents the entity mention (multiple entity mentions in the article can refer to the sameconcept).candidates: the candidate Wikipedia links.scores: the prior probabilities of thecandidatesentity links calculated on Wikipedia corpus.
concepts: a list of entities that cluster each of the entitymentions. Each entity is annotated with the following keys:concept: the unique document-level entity id.text: the text of the longest mention that belong to the entity.keyword: indicates whether the entity is a keyword.count: the number of entity mentions in the document that belong to the entity.link: the entity link to Wikipedia.tags: multi-label classification labels associated to the entity.
relations: a list of document-level relations between entities (concepts). Each of the relations is annotated with the following keys:s: the subject entity id involved in the relation.p: the predicate that defines the relation name (i.e., "citizen_of", "member_of", etc.).o: the object entity id involved in the relation.
iptc: multi-label article IPTC classification codes. For detailed meaning of each of the codes, please refer to the official IPTC code list.
To train and run the models from our paper, we recommend creating a separate environment. For example:
conda create -n dwie-paper python=3.8
conda activate dwie-paper
pip install -r requirements.txt
Next, initial files (e.g., embeddings) and configurations have to be set up by running the following script:
./setup_models.sh
Make sure that the directory structure is as follows:
├── experiments
├── data
│ ├── annos
│ ├── annos_with_content
│ └── embeddings
├── src
The training script is located in src/model_train.py, it takes two arguments:
1- --config_file: the configuration file to run one of the experiments in experiments directory
(the names of experiment config files are self explanatory and correspond to Table 9 of the
paper).
2- --path: the directory where the output model, results and tensorboard logs are going to be
saved. For each experiment, a separate subdirectory with the name of experiment is automatically
created.
Example:
python src/model_train.py --config_file experiments/01_single_coref.json --path results
After the training (see above) is finished, the prediction files are saved as test.json inside the results
directory. In order to obtain the F1 scores execute:
python -u src/dwie_evaluation.py --pred [path to prediction file] --gold data/annos/ --gold-filter test
Example:
python -u src/dwie_evaluation.py --pred results/01_single_coref/test.json --gold data/annos/ --gold-filter test
The test.json can be also obtained by explicitly loading and evaluating the model in the results directory:
python -u src/model_predict.py --path [path to the results directory with serialized model savemodel.pth]
Example:
python -u src/model_predict.py --path results/01_single_coref/
We provide the evaluation script src/dwie_evaluation.py in order to obtain all the metrics defined in
our paper. The unit test cases are located in src/tests/ directory.
The following is an illustrative example of how to use the evaluation script
on one predicted (predicted.json) and the respective
ground truth (ground_truth.json) annotation files:
from dwie_evaluation import load_json, EvaluatorDWIE
dwie_eval = EvaluatorDWIE()
loaded_ground_truth = load_json('ground_truth.json', None)
loaded_predicted = load_json('predicted.json', None)
for article_id in loaded_ground_truth.keys():
dwie_eval.add(loaded_predicted[article_id], loaded_ground_truth[article_id])
# Coreference Metrics
print('Coref MUC F1:', dwie_eval.coref_muc.get_f1())
print('Coref B-Cubed F1:', dwie_eval.coref_bcubed.get_f1())
print('Coref CEAFe F1:', dwie_eval.coref_ceafe.get_f1())
print('Coref Avg.:', sum([dwie_eval.coref_muc.get_f1(), dwie_eval.coref_bcubed.get_f1(), \
dwie_eval.coref_ceafe.get_f1()]) / 3)
# NER Metrics
print('NER Mention-Level F1:', dwie_eval.tags_mention.get_f1())
print('NER Hard Entity-Level F1:', dwie_eval.tags_hard.get_f1())
print('NER Soft Entity-Level F1:', dwie_eval.tags_soft.get_f1())
# Relation Extraction (RE) Metrics
print('RE Mention-Level F1:', dwie_eval.rels_mention.get_f1())
print('RE Hard Entity-Level F1:', dwie_eval.rels_hard.get_f1())
print('RE Soft Entity-Level F1:', dwie_eval.rels_soft.get_f1())
Should you use this code/dataset for your own research, please cite:
@article{ZAPOROJETS2021102563,
title = {{DWIE}: An entity-centric dataset for multi-task document-level information extraction},
journal = {Information Processing & Management},
volume = {58},
number = {4},
pages = {102563},
year = {2021},
issn = {0306-4573},
doi = {https://doi.org/10.1016/j.ipm.2021.102563},
url = {https://www.sciencedirect.com/science/article/pii/S0306457321000662},
author = {Klim Zaporojets and Johannes Deleu and Chris Develder and Thomas Demeester}
}
If you have questions using DWIE, please e-mail us at klim.zaporojets@ugent.be
Part of the research leading to DWIE dataset has received funding from (i) the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 761488 for the CPN project, and (ii) the Flemish Government under the "Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen" programme.