File size: 9,379 Bytes
42e6fa5 d963723 42e6fa5 d963723 42e6fa5 d963723 42e6fa5 d963723 42e6fa5 |
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 |
# 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.
"""Descriptions of genetic variations and their effect are widely spread across the biomedical literature. However,
finding all mentions of a specific variation, or all mentions of variations in a specific gene, is difficult to
achieve due to the many ways such variations are described. Here, we describe SETH, a tool for the recognition of
variations from text and their subsequent normalization to dbSNP or UniProt. SETH achieves high precision and recall
on several evaluation corpora of PubMed abstracts. It is freely available and encompasses stand-alone scripts for
isolated application and evaluation as well as a thorough documentation for integration into other applications.
The script loads dataset in bigbio schema (using knowledgebase schema: schemas/kb) AND/OR source (default) schema """
from pathlib import Path
from typing import Dict, List, Tuple
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
_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@Article{SETH2016,
Title = {SETH detects and normalizes genetic variants in text.},
Author = {Thomas, Philippe and Rockt{"{a}}schel, Tim and Hakenberg, J{"{o}}rg and Lichtblau, Yvonne and Leser, Ulf},
Journal = {Bioinformatics},
Year = {2016},
Month = {Jun},
Doi = {10.1093/bioinformatics/btw234},
Language = {eng},
Medline-pst = {aheadofprint},
Pmid = {27256315},
Url = {http://dx.doi.org/10.1093/bioinformatics/btw234
}
"""
_DATASETNAME = "seth_corpus"
_DISPLAYNAME = "SETH Corpus"
_DESCRIPTION = (
"""SNP named entity recognition corpus consisting of 630 PubMed citations."""
)
_HOMEPAGE = "https://github.com/rockt/SETH"
_LICENSE = 'Apache License 2.0'
_URLS = {
"source": "https://github.com/rockt/SETH/archive/refs/heads/master.zip",
"bigbio_kb": "https://github.com/rockt/SETH/archive/refs/heads/master.zip",
}
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class SethCorpusDataset(datasets.GeneratorBasedBuilder):
"""SNP named entity recognition corpus consisting of 630 PubMed citations."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="seth_corpus_source",
version=SOURCE_VERSION,
description="SETH corpus source schema",
schema="source",
subset_id="seth_corpus",
),
BigBioConfig(
name="seth_corpus_bigbio_kb",
version=BIGBIO_VERSION,
description="SETH corpus BigBio schema",
schema="bigbio_kb",
subset_id="seth_corpus",
),
]
DEFAULT_CONFIG_NAME = "seth_corpus_source"
def _info(self) -> datasets.DatasetInfo:
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"),
"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"),
"cuid": datasets.Value("string"),
"text": 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) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
urls = _URLS[self.config.schema]
data_dir = Path(dl_manager.download_and_extract(urls))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir / "SETH-master" / "resources" / "SETH-corpus",
"corpus_file": "corpus.txt",
"split": "train",
},
),
]
def _generate_examples(
self, filepath: Path, corpus_file: str, split: str
) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
if self.config.schema == "source":
with open(filepath / corpus_file, encoding="utf-8") as f:
contents = f.readlines()
for guid, content in enumerate(contents):
file_name, text = content.split("\t")
example = parse_brat_file(
filepath / "annotations" / f"{file_name}.ann"
)
example["id"] = str(guid)
example["text"] = text
yield guid, example
elif self.config.schema == "bigbio_kb":
with open(filepath / corpus_file, encoding="utf-8") as f:
contents = f.readlines()
for guid, content in enumerate(contents):
file_name, text = content.split("\t")
example = parse_brat_file(
filepath / "annotations" / f"{file_name}.ann"
)
# this example contains event lines
# but events have not arguments
# this is most likely an error on the annotation side
if example["document_id"] == "11058905":
example["events"] = []
example["text"] = text
example = brat_parse_to_bigbio_kb(example)
example["id"] = str(guid)
yield guid, example
else:
raise ValueError(f"Invalid config: {self.config.name}")
|