File size: 9,766 Bytes
9202844 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 |
# 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.
from pathlib import Path
from typing import Dict, List, Tuple, Union
import datasets
import jsonlines
from .bigbiohub import kb_features, BigBioConfig, Tasks
_CITATION = """\
@InProceedings{wuehrl_klinger_2022,
author = {Wuehrl, Amelie and Klinger, Roman},
title = {Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)},
booktitle = {Proceedings of The 13th Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association}
}
"""
_DATASETNAME = "bear"
_DISPLAYNAME = "BEAR"
_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_LICENSE = "CC_BY_SA_4p0"
_DESCRIPTION = """\
A dataset of 2100 Twitter posts annotated with 14 different types of biomedical entities (e.g., disease, treatment,
risk factor, etc.) and 20 relation types (including caused, treated, worsens, etc.).
"""
_HOMEPAGE = "https://www.ims.uni-stuttgart.de/en/research/resources/corpora/bioclaim/"
_URLS = {
_DATASETNAME: "https://www.ims.uni-stuttgart.de/documents/ressourcen/korpora/bioclaim/bear-corpus-WuehrlKlinger-\
LREC2022.zip",
}
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class BearDataset(datasets.GeneratorBasedBuilder):
"""
BEAR: A Corpus of Biomedical Entities and Relations
A dataset of 2100 Twitter posts annotated with 14 different types of
biomedical entities (e.g., disease, treatment, risk factor, etc.)
and 20 relation types (including caused, treated, worsens, etc.).
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="bear_source",
version=SOURCE_VERSION,
description="bear source schema",
schema="source",
subset_id="bear",
),
BigBioConfig(
name="bear_bigbio_kb",
version=BIGBIO_VERSION,
description="bear BigBio schema",
schema="bigbio_kb",
subset_id="bear",
),
]
DEFAULT_CONFIG_NAME = "bear_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"document_id": datasets.Value("string"),
"document_text": datasets.Value("string"),
"entities": [
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"text": datasets.Value("string"),
"offsets": datasets.Sequence(datasets.Value("int32")),
}
],
"relations": [
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"arg1_id": datasets.Value("string"),
"arg2_id": datasets.Value("string"),
}
],
}
)
elif self.config.schema == "bigbio_kb":
features = kb_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
urls = _URLS[_DATASETNAME]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": Path(data_dir) / "corpus" / "bear.jsonl",
},
),
]
def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
uid = 0
input_file = filepath
with jsonlines.open(input_file, "r") as file:
for document in file:
document_id: str = document.pop("doc_id")
document_text: str = document.pop("doc_text")
entities: Dict[str, Dict[str, Union[str, int]]] = document.pop("entities", {})
relations: List[Dict[str, Union[str, int]]] = document.pop("relations", [])
if not entities and not relations:
continue
if self.config.schema == "source":
source_example = self._to_source_example(
document_id=document_id,
document_text=document_text,
entities=entities,
relations=relations,
)
yield uid, source_example
elif self.config.schema == "bigbio_kb":
bigbio_example = self._to_bigbio_example(
document_id=document_id,
document_text=document_text,
entities=entities,
relations=relations,
)
yield uid, bigbio_example
uid += 1
def _to_source_example(
self,
document_id: str,
document_text: str,
entities: Dict[str, Dict[str, Union[str, int]]],
relations: List[Dict[str, Union[str, int]]],
) -> Dict:
source_example = {
"document_id": document_id,
"document_text": document_text,
}
# Capture Entities
_entities = []
for id, entity_values in entities.items():
if not entity_values:
continue
start = entity_values.pop("begin")
end = entity_values.pop("end")
type = entity_values.pop("tag")
text = document_text[start:end]
entity = {
"id": f"{document_id}_{id}",
"type": type,
"text": text,
"offsets": [start, end],
}
_entities.append(entity)
source_example["entities"] = _entities
# Capture Relations
_relations = []
for id, relation_values in enumerate(relations):
end_entity = relation_values.pop("end_entity")
rel_tag = relation_values.pop("rel_tag")
start_entity = relation_values.pop("start_entity")
relation = {
"id": f"{document_id}_relation_{id}",
"type": rel_tag,
"arg1_id": f"{document_id}_{start_entity}",
"arg2_id": f"{document_id}_{end_entity}",
}
_relations.append(relation)
source_example["relations"] = _relations
return source_example
def _to_bigbio_example(
self,
document_id: str,
document_text: str,
entities: Dict[str, Dict[str, Union[str, int]]],
relations: List[Dict[str, Union[str, int]]],
) -> Dict:
bigbio_example = {
"id": f"{document_id}_id",
"document_id": document_id,
"passages": [
{
"id": f"{document_id}_passage",
"type": "social_media_text",
"text": [document_text],
"offsets": [[0, len(document_text)]],
}
],
"events": [],
"coreferences": [],
}
# Capture Entities
_entities = []
for id, entity_values in entities.items():
if not entity_values:
continue
start = entity_values.pop("begin")
end = entity_values.pop("end")
type = entity_values.pop("tag")
text = document_text[start:end]
entity = {
"id": f"{document_id}_{id}",
"type": type,
"text": [text],
"offsets": [[start, end]],
"normalized": [],
}
_entities.append(entity)
bigbio_example["entities"] = _entities
# Capture Relations
_relations = []
for id, relation_values in enumerate(relations):
end_entity = relation_values.pop("end_entity")
rel_tag = relation_values.pop("rel_tag")
start_entity = relation_values.pop("start_entity")
relation = {
"id": f"{document_id}_relation_{id}",
"type": rel_tag,
"arg1_id": f"{document_id}_{start_entity}",
"arg2_id": f"{document_id}_{end_entity}",
"normalized": [],
}
_relations.append(relation)
bigbio_example["relations"] = _relations
return bigbio_example
|