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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
from typing import List
import datasets
from .bigbiohub import kb_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks
from .bigbiohub import parse_brat_file
from .bigbiohub import brat_parse_to_bigbio_kb
_DATASETNAME = "bionlp_st_2013_ge"
_DISPLAYNAME = "BioNLP 2013 GE"
_SOURCE_VIEW_NAME = "source"
_UNIFIED_VIEW_NAME = "bigbio"
_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@inproceedings{kim-etal-2013-genia,
title = "The {G}enia Event Extraction Shared Task, 2013 Edition - Overview",
author = "Kim, Jin-Dong and
Wang, Yue and
Yasunori, Yamamoto",
booktitle = "Proceedings of the {B}io{NLP} Shared Task 2013 Workshop",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-2002",
pages = "8--15",
}
"""
_DESCRIPTION = """\
The BioNLP-ST GE task has been promoting development of fine-grained
information extraction (IE) from biomedical
documents, since 2009. Particularly, it has focused on the domain of
NFkB as a model domain of Biomedical IE
"""
_HOMEPAGE = "https://github.com/openbiocorpora/bionlp-st-2013-ge"
_LICENSE = 'GENIA Project License for Annotated Corpora'
_URLs = {
"source": "https://github.com/openbiocorpora/bionlp-st-2013-ge/archive/refs/heads/master.zip",
"bigbio_kb": "https://github.com/openbiocorpora/bionlp-st-2013-ge/archive/refs/heads/master.zip",
}
_SUPPORTED_TASKS = [
Tasks.EVENT_EXTRACTION,
Tasks.NAMED_ENTITY_RECOGNITION,
Tasks.RELATION_EXTRACTION,
Tasks.COREFERENCE_RESOLUTION,
]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class bionlp_st_2013_ge(datasets.GeneratorBasedBuilder):
"""The BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical
documents, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="bionlp_st_2013_ge_source",
version=SOURCE_VERSION,
description="bionlp_st_2013_ge source schema",
schema="source",
subset_id="bionlp_st_2013_ge",
),
BigBioConfig(
name="bionlp_st_2013_ge_bigbio_kb",
version=BIGBIO_VERSION,
description="bionlp_st_2013_ge BigBio schema",
schema="bigbio_kb",
subset_id="bionlp_st_2013_ge",
),
]
DEFAULT_CONFIG_NAME = "bionlp_st_2013_ge_source"
def _info(self):
"""
- `features` defines the schema of the parsed data set. The schema depends on the
chosen `config`: If it is `_SOURCE_VIEW_NAME` the schema is the schema of the
original data. If `config` is `_UNIFIED_VIEW_NAME`, then the schema is the
canonical KB-task schema defined in `biomedical/schemas/kb.py`.
"""
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"text": datasets.Value("string"),
"text_bound_annotations": [ # T line in brat, e.g. type or event trigger
{
"offsets": datasets.Sequence([datasets.Value("int32")]),
"text": datasets.Sequence(datasets.Value("string")),
"type": datasets.Value("string"),
"id": datasets.Value("string"),
}
],
"events": [ # E line in brat
{
"trigger": datasets.Value(
"string"
), # refers to the text_bound_annotation of the trigger,
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"arguments": datasets.Sequence(
{
"role": datasets.Value("string"),
"ref_id": datasets.Value("string"),
}
),
}
],
"relations": [ # R line in brat
{
"id": datasets.Value("string"),
"head": {
"ref_id": datasets.Value("string"),
"role": datasets.Value("string"),
},
"tail": {
"ref_id": datasets.Value("string"),
"role": datasets.Value("string"),
},
"type": datasets.Value("string"),
}
],
"equivalences": [ # Equiv line in brat
{
"id": datasets.Value("string"),
"ref_ids": datasets.Sequence(datasets.Value("string")),
}
],
"attributes": [ # M or A lines in brat
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"ref_id": datasets.Value("string"),
"value": datasets.Value("string"),
}
],
"normalizations": [ # N lines in brat
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"ref_id": datasets.Value("string"),
"resource_name": datasets.Value(
"string"
), # Name of the resource, e.g. "Wikipedia"
"cuid": datasets.Value(
"string"
), # ID in the resource, e.g. 534366
"text": datasets.Value(
"string"
), # Human readable description/name of the entity, e.g. "Barack Obama"
}
],
},
)
elif self.config.schema == "bigbio_kb":
features = kb_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=str(_LICENSE),
citation=_CITATION,
)
def _split_generators(
self, dl_manager: datasets.DownloadManager
) -> List[datasets.SplitGenerator]:
my_urls = _URLs[self.config.schema]
data_dir = Path(dl_manager.download_and_extract(my_urls))
data_files = {
"train": data_dir / f"bionlp-st-2013-ge-master" / "original-data" / "train",
"dev": data_dir / f"bionlp-st-2013-ge-master" / "original-data" / "devel",
"test": data_dir / f"bionlp-st-2013-ge-master" / "original-data" / "test",
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_files": data_files["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"data_files": data_files["dev"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"data_files": data_files["test"]},
),
]
def _generate_examples(self, data_files: Path):
if self.config.schema == "source":
txt_files = list(data_files.glob("*txt"))
for guid, txt_file in enumerate(txt_files):
example = parse_brat_file(txt_file)
example["id"] = str(guid)
yield guid, example
elif self.config.schema == "bigbio_kb":
txt_files = list(data_files.glob("*txt"))
for guid, txt_file in enumerate(txt_files):
example = brat_parse_to_bigbio_kb(
parse_brat_file(txt_file)
)
example["id"] = str(guid)
yield guid, example
else:
raise ValueError(f"Invalid config: {self.config.name}")
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