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# coding=utf-8
# Copyright 2022 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.
"""
A corpus for plant and chemical entities and for the relationships between them.
The corpus contains 2218 plant and chemical entities and 600 plant-chemical
relationships which are drawn from 1109 sentences in 245 PubMed abstracts.
"""
from pathlib import Path
from typing import Dict, Iterator, Tuple
import datasets
from .bigbiohub import kb_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks
_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@article{choi2016corpus,
title = {A corpus for plant-chemical relationships in the biomedical domain},
author = {
Choi, Wonjun and Kim, Baeksoo and Cho, Hyejin and Lee, Doheon and Lee,
Hyunju
},
year = 2016,
journal = {BMC bioinformatics},
publisher = {Springer},
volume = 17,
number = 1,
pages = {1--15}
}
"""
_DATASETNAME = "pcr"
_DISPLAYNAME = "PCR"
_DESCRIPTION = """
A corpus for plant / herb and chemical entities and for the relationships \
between them. The corpus contains 2218 plant and chemical entities and 600 \
plant-chemical relationships which are drawn from 1109 sentences in 245 PubMed \
abstracts.
"""
_HOMEPAGE = "http://210.107.182.73/plantchemcorpus.htm"
_LICENSE = 'License information unavailable'
_URLS = {_DATASETNAME: "http://210.107.182.73/1109_corpus_units_STformat.tar"}
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.EVENT_EXTRACTION]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class PCRDataset(datasets.GeneratorBasedBuilder):
"""
The corpus of plant-chemical relation consists of plants / herbs and
chemicals and relations between them.
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="pcr_source",
version=SOURCE_VERSION,
description="PCR source schema",
schema="source",
subset_id="pcr",
),
BigBioConfig(
name="pcr_fixed_source",
version=SOURCE_VERSION,
description="PCR (with fixed offsets) source schema",
schema="source",
subset_id="pcr_fixed",
),
BigBioConfig(
name="pcr_bigbio_kb",
version=BIGBIO_VERSION,
description="PCR BigBio schema",
schema="bigbio_kb",
subset_id="pcr",
),
]
DEFAULT_CONFIG_NAME = "pcr_source"
def _info(self):
if self.config.schema == "source":
features = datasets.Features(
{
"document_id": datasets.Value("string"),
"text": datasets.Value("string"),
"entities": [
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"offsets": datasets.Sequence([datasets.Value("int32")]),
"text": datasets.Sequence(datasets.Value("string")),
"normalized": [
{
"db_name": datasets.Value("string"),
"db_id": datasets.Value("string"),
}
],
}
],
"events": [
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
# refers to the text_bound_annotation of the trigger
"trigger": {
"text": datasets.Sequence(datasets.Value("string")),
"offsets": datasets.Sequence([datasets.Value("int32")]),
},
"arguments": [
{
"role": datasets.Value("string"),
"ref_id": datasets.Value("string"),
}
],
}
],
},
)
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):
urls = _URLS[_DATASETNAME]
data_dir = Path(dl_manager.download_and_extract(urls))
data_dir = data_dir / "1109 corpus units"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_dir": data_dir},
)
]
def _generate_examples(self, data_dir: Path) -> Iterator[Tuple[str, Dict]]:
if self.config.schema == "source":
for file in data_dir.iterdir():
if not str(file).endswith(".txt"):
continue
example = parsing.parse_brat_file(file)
example = parsing.brat_parse_to_bigbio_kb(example)
example = self._to_source_example(example)
# Three documents have incorrect offsets - fix them for fixed_source scheme
if self.config.subset_id == "pcr_fixed" and example["document_id"] in [
"463",
"509",
"566",
]:
example = self._fix_example(example)
yield example["document_id"], example
elif self.config.schema == "bigbio_kb":
for file in data_dir.iterdir():
if not str(file).endswith(".txt"):
continue
example = parsing.parse_brat_file(file)
example = parsing.brat_parse_to_bigbio_kb(example)
document_id = example["document_id"]
example["id"] = document_id
# Three documents have incorrect offsets - fix them for BigBio scheme
if document_id in ["463", "509", "566"]:
example = self._fix_example(example)
yield example["id"], example
def _to_source_example(self, bigbio_example: Dict) -> Dict:
"""
Converts an example in BigBio-KB scheme to an example according to the source scheme
"""
source_example = bigbio_example.copy()
source_example["text"] = bigbio_example["passages"][0]["text"][0]
source_example.pop("passages", None)
source_example.pop("relations", None)
source_example.pop("coreferences", None)
return source_example
def _fix_example(self, example: Dict) -> Dict:
"""
Fixes by the example by adapting the offsets of the trigger word of the first
event. In the official annotation data the end offset is incorrect (for 3 examples).
"""
first_event = example["events"][0]
trigger_text = first_event["trigger"]["text"][0]
offsets = first_event["trigger"]["offsets"][0]
real_offsets = [offsets[0], offsets[0] + len(trigger_text)]
example["events"][0]["trigger"]["offsets"] = [real_offsets]
return example
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