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# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""NCBI disease corpus: a resource for disease name recognition and concept normalization"""
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{dougan2014ncbi,
title={NCBI disease corpus: a resource for disease name recognition and concept normalization},
author={Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
journal={Journal of biomedical informatics},
volume={47},
pages={1--10},
year={2014},
publisher={Elsevier}
}
"""
_DESCRIPTION = """\
This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed
abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural
language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions
and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®).
Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations.
Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two
annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked
against annotations of the rest of the corpus to assure corpus-wide consistency.
For more details, see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655/
The original dataset can be downloaded from: https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBI_corpus.zip
This dataset has been converted to CoNLL format for NER using the following tool: https://github.com/spyysalo/standoff2conll
Note: there is a duplicate document (PMID 8528200) in the original data, and the duplicate is recreated in the converted data.
"""
_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655/"
_URL = "https://github.com/spyysalo/ncbi-disease/raw/master/conll/"
_TRAINING_FILE = "train.tsv"
_DEV_FILE = "devel.tsv"
_TEST_FILE = "test.tsv"
class NCBIDiseaseConfig(datasets.BuilderConfig):
"""BuilderConfig for NCBIDisease"""
def __init__(self, **kwargs):
"""BuilderConfig for NCBIDisease.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(NCBIDiseaseConfig, self).__init__(**kwargs)
class NCBIDisease(datasets.GeneratorBasedBuilder):
"""NCBIDisease dataset."""
BUILDER_CONFIGS = [
NCBIDiseaseConfig(name="ncbi_disease", version=datasets.Version("1.0.0"), description="NCBIDisease dataset"),
]
def _info(self):
custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE',
'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-CELL_LINE', 'I-CELL_LINE', 'B-CELL_TYPE', 'I-CELL_TYPE',
'B-PROTEIN', 'I-PROTEIN', 'B-SPECIES', 'I-SPECIES']
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=custom_names
)
),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
"dev": f"{_URL}{_DEV_FILE}",
"test": f"{_URL}{_TEST_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
ner_tags = []
for line in f:
if line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
ner_tags = []
else:
# tokens are tab separated
splits = line.split("\t")
tokens.append(splits[0])
if(splits[1].rstrip()=="B-Disease"):
ner_tags.append("B-DISEASE")
elif(splits[1].rstrip()=="I-Disease"):
ner_tags.append("I-DISEASE")
else:
ner_tags.append(splits[1].rstrip())
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
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