Datasets:
Tasks:
Text Retrieval
Sub-tasks:
entity-linking-retrieval
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
crowdsourced
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 5,322 Bytes
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"""DocRED: A Large-Scale Document-Level Relation Extraction Dataset"""
import json
import datasets
_CITATION = """\
@inproceedings{yao-etal-2019-docred,
title = "{D}oc{RED}: A Large-Scale Document-Level Relation Extraction Dataset",
author = "Yao, Yuan and
Ye, Deming and
Li, Peng and
Han, Xu and
Lin, Yankai and
Liu, Zhenghao and
Liu, Zhiyuan and
Huang, Lixin and
Zhou, Jie and
Sun, Maosong",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1074",
doi = "10.18653/v1/P19-1074",
pages = "764--777",
}
"""
_DESCRIPTION = """\
Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by \
existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single \
entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed \
from Wikipedia and Wikidata with three features:
- DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.
- DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.
- Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios.
"""
_URLS = {
"dev": "data/dev.json.gz",
"train_distant": "data/train_distant.json.gz",
"train_annotated": "data/train_annotated.json.gz",
"test": "data/test.json.gz",
"rel_info": "data/rel_info.json.gz",
}
class DocRed(datasets.GeneratorBasedBuilder):
"""DocRED: A Large-Scale Document-Level Relation Extraction Dataset"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"title": datasets.Value("string"),
"sents": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
"vertexSet": [
[
{
"name": datasets.Value("string"),
"sent_id": datasets.Value("int32"),
"pos": datasets.features.Sequence(datasets.Value("int32")),
"type": datasets.Value("string"),
}
]
],
"labels": datasets.features.Sequence(
{
"head": datasets.Value("int32"),
"tail": datasets.Value("int32"),
"relation_id": datasets.Value("string"),
"relation_text": datasets.Value("string"),
"evidence": datasets.features.Sequence(datasets.Value("int32")),
}
),
}
),
supervised_keys=None,
homepage="https://github.com/thunlp/DocRED",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloads = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": downloads["dev"], "rel_info": downloads["rel_info"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"], "rel_info": downloads["rel_info"]}
),
datasets.SplitGenerator(
name="train_annotated",
gen_kwargs={"filepath": downloads["train_annotated"], "rel_info": downloads["rel_info"]},
),
datasets.SplitGenerator(
name="train_distant",
gen_kwargs={"filepath": downloads["train_distant"], "rel_info": downloads["rel_info"]},
),
]
def _generate_examples(self, filepath, rel_info):
"""Generate DocRED examples."""
with open(rel_info, encoding="utf-8") as f:
relation_name_map = json.load(f)
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for idx, example in enumerate(data):
# Test set has no labels - Results need to be uploaded to Codalab
if "labels" not in example.keys():
example["labels"] = []
for label in example["labels"]:
# Rename and include full relation names
label["relation_text"] = relation_name_map[label["r"]]
label["relation_id"] = label["r"]
label["head"] = label["h"]
label["tail"] = label["t"]
del label["r"]
del label["h"]
del label["t"]
yield idx, example
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