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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.
import collections
import itertools
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from bioc import biocxml
from .bigbiohub import kb_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks
from .bigbiohub import get_texts_and_offsets_from_bioc_ann
_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@article{islamaj2021nlm,
title = {
NLM-Gene, a richly annotated gold standard dataset for gene entities that
addresses ambiguity and multi-species gene recognition
},
author = {
Islamaj, Rezarta and Wei, Chih-Hsuan and Cissel, David and Miliaras,
Nicholas and Printseva, Olga and Rodionov, Oleg and Sekiya, Keiko and Ward,
Janice and Lu, Zhiyong
},
year = 2021,
journal = {Journal of Biomedical Informatics},
publisher = {Elsevier},
volume = 118,
pages = 103779
}
"""
_DATASETNAME = "nlm_gene"
_DISPLAYNAME = "NLM-Gene"
_DESCRIPTION = """\
NLM-Gene consists of 550 PubMed articles, from 156 journals, and contains more \
than 15 thousand unique gene names, corresponding to more than five thousand \
gene identifiers (NCBI Gene taxonomy). This corpus contains gene annotation data \
from 28 organisms. The annotated articles contain on average 29 gene names, and \
10 gene identifiers per article. These characteristics demonstrate that this \
article set is an important benchmark dataset to test the accuracy of gene \
recognition algorithms both on multi-species and ambiguous data. The NLM-Gene \
corpus will be invaluable for advancing text-mining techniques for gene \
identification tasks in biomedical text.
"""
_HOMEPAGE = "https://zenodo.org/record/5089049"
_LICENSE = 'Creative Commons Zero v1.0 Universal'
_URLS = {
"source": "https://zenodo.org/record/5089049/files/NLM-Gene-Corpus.zip",
"bigbio_kb": "https://zenodo.org/record/5089049/files/NLM-Gene-Corpus.zip",
}
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class NLMGeneDataset(datasets.GeneratorBasedBuilder):
"""NLM-Gene Dataset for gene entities"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="nlm_gene_source",
version=SOURCE_VERSION,
description="NlM Gene source schema",
schema="source",
subset_id="nlm_gene",
),
BigBioConfig(
name="nlm_gene_bigbio_kb",
version=BIGBIO_VERSION,
description="NlM Gene BigBio schema",
schema="bigbio_kb",
subset_id="nlm_gene",
),
]
DEFAULT_CONFIG_NAME = "nlm_gene_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
if self.config.schema == "source":
# this is a variation on the BioC format
features = datasets.Features(
{
"passages": [
{
"document_id": datasets.Value("string"),
"type": datasets.Value("string"),
"text": datasets.Value("string"),
"entities": [
{
"id": datasets.Value("string"),
"offsets": [[datasets.Value("int32")]],
"text": [datasets.Value("string")],
"type": datasets.Value("string"),
"normalized": [
{
"db_name": datasets.Value("string"),
"db_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) -> 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 / "Corpus",
"file_name": "Pmidlist.Train.txt",
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": data_dir / "Corpus",
"file_name": "Pmidlist.Test.txt",
"split": "test",
},
),
]
@staticmethod
def _get_bioc_entity(
span, db_id_key="NCBI Gene identifier", splitters=",;|-"
) -> dict:
"""Parse BioC entity annotation."""
offsets, texts = get_texts_and_offsets_from_bioc_ann(span)
db_ids = span.infons.get(db_id_key, "-1")
# Correct an annotation error in PMID 24886643
if db_ids.startswith('-222'):
db_ids = db_ids.lstrip('-222,')
# No listed entity for a mention
if db_ids in ['-1','-000','-111','-']:
normalized = []
else:
# Find connector between db_ids for the normalization, if not found, use default
connector = "|"
for splitter in list(splitters):
if splitter in db_ids:
connector = splitter
normalized = [
{"db_name": "NCBIGene", "db_id": db_id} for db_id in db_ids.split(connector)
]
return {
"id": span.id,
"offsets": offsets,
"text": texts,
"type": span.infons["type"],
"normalized": normalized,
}
def _generate_examples(
self, filepath: Path, file_name: str, split: str
) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
if self.config.schema == "source":
with open(filepath / file_name, encoding="utf-8") as f:
contents = f.readlines()
for uid, content in enumerate(contents):
file_id = content.replace("\n", "")
file_path = filepath / "FINAL" / f"{file_id}.BioC.XML"
reader = biocxml.BioCXMLDocumentReader(file_path.as_posix())
for xdoc in reader:
yield uid, {
"passages": [
{
"document_id": xdoc.id,
"type": passage.infons["type"],
"text": passage.text,
"entities": [
self._get_bioc_entity(span)
for span in passage.annotations
],
}
for passage in xdoc.passages
]
}
elif self.config.schema == "bigbio_kb":
with open(filepath / file_name, encoding="utf-8") as f:
contents = f.readlines()
uid = 0 # global unique id
for i, content in enumerate(contents):
file_id = content.replace("\n", "")
file_path = filepath / "FINAL" / f"{file_id}.BioC.XML"
reader = biocxml.BioCXMLDocumentReader(file_path.as_posix())
for xdoc in reader:
data = {
"id": uid,
"document_id": xdoc.id,
"passages": [],
"entities": [],
"relations": [],
"events": [],
"coreferences": [],
}
uid += 1
char_start = 0
# passages must not overlap and spans must cover the entire document
for passage in xdoc.passages:
offsets = [[char_start, char_start + len(passage.text)]]
char_start = char_start + len(passage.text) + 1
data["passages"].append(
{
"id": uid,
"type": passage.infons["type"],
"text": [passage.text],
"offsets": offsets,
}
)
uid += 1
# entities
for passage in xdoc.passages:
for span in passage.annotations:
ent = self._get_bioc_entity(
span, db_id_key="NCBI Gene identifier"
)
ent["id"] = uid # override BioC default id
data["entities"].append(ent)
uid += 1
yield i, data
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