Hugging Face's logo

Datasets:
bigbio
/
ncbi_disease

Languages:
English
Multilinguality:
monolingual
License:
Dataset card Files Community
3
ncbi_disease / ncbi_disease.py
gabrielaltay's picture
gabrielaltay
fix close paren typo (#3)
b96b632
raw history blame contribute delete
No virus
10.3 kB
# 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.
"""
The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research
resource for the biomedical natural language processing community.
"""
import os
from typing import Dict, Iterator, List, Tuple
import datasets
from bioc import pubtator
from .bigbiohub import kb_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks
_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@article{Dogan2014NCBIDC,
title = {NCBI disease corpus: A resource for disease name recognition and concept normalization},
author = {Rezarta Islamaj Dogan and Robert Leaman and Zhiyong Lu},
year = 2014,
journal = {Journal of biomedical informatics},
volume = 47,
pages = {1--10}
}
"""
_DATASETNAME = "ncbi_disease"
_DISPLAYNAME = "NCBI Disease"
_DESCRIPTION = """\
The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research
resource for the biomedical natural language processing community.
"""
_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/"
_LICENSE = 'Creative Commons Zero v1.0 Universal'
_URLS = {
_DATASETNAME: {
datasets.Split.TRAIN: "https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBItrainset_corpus.zip",
datasets.Split.TEST: "https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBItestset_corpus.zip",
datasets.Split.VALIDATION: "https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBIdevelopset_corpus.zip",
}
}
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class NCBIDiseaseDataset(datasets.GeneratorBasedBuilder):
"""NCBI Disease"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="ncbi_disease_source",
version=SOURCE_VERSION,
description="NCBI Disease source schema",
schema="source",
subset_id="ncbi_disease",
),
BigBioConfig(
name="ncbi_disease_bigbio_kb",
version=BIGBIO_VERSION,
description="NCBI Disease BigBio schema",
schema="bigbio_kb",
subset_id="ncbi_disease",
),
]
DEFAULT_CONFIG_NAME = "ncbi_disease_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"pmid": datasets.Value("string"),
"title": datasets.Value("string"),
"abstract": datasets.Value("string"),
"mentions": [
{
"concept_id": datasets.Value("string"),
"type": datasets.Value("string"),
"text": datasets.Value("string"),
"offsets": datasets.Sequence(datasets.Value("int32")),
}
],
}
)
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]:
urls = _URLS[_DATASETNAME]
data_dir = dl_manager.download_and_extract(urls)
train_filename = "NCBItrainset_corpus.txt"
test_filename = "NCBItestset_corpus.txt"
dev_filename = "NCBIdevelopset_corpus.txt"
train_filepath = os.path.join(data_dir[datasets.Split.TRAIN], train_filename)
test_filepath = os.path.join(data_dir[datasets.Split.TEST], test_filename)
dev_filepath = os.path.join(data_dir[datasets.Split.VALIDATION], dev_filename)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": train_filepath,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": test_filepath,
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": dev_filepath,
"split": "dev",
},
),
]
def _generate_examples(
self, filepath: str, split: str
) -> Iterator[Tuple[str, Dict]]:
if self.config.schema == "source":
for i, source_example in enumerate(self._pubtator_to_source(filepath)):
# Some examples are duplicated in NCBI Disease. We have to make them unique to
# avoid and error from datasets.
yield str(i) + "_" + source_example["pmid"], source_example
elif self.config.schema == "bigbio_kb":
seen = []
for kb_example in self._pubtator_to_bigbio_kb(filepath):
# Some examples are duplicated in NCBI Disease. Avoid yielding more than once.
if kb_example["id"] in seen:
continue
yield kb_example["id"], kb_example
seen.append(kb_example["id"])
@staticmethod
def _pubtator_to_source(filepath: Dict) -> Iterator[Dict]:
with open(filepath, "r") as f:
for doc in pubtator.iterparse(f):
source_example = {
"pmid": doc.pmid,
"title": doc.title,
"abstract": doc.abstract,
"mentions": [
{
"concept_id": mention.id,
"type": mention.type,
"text": mention.text,
"offsets": [mention.start, mention.end],
}
for mention in doc.annotations
],
}
yield source_example
@staticmethod
def _pubtator_to_bigbio_kb(filepath: Dict) -> Iterator[Dict]:
with open(filepath, "r") as f:
unified_example = {}
for doc in pubtator.iterparse(f):
unified_example["id"] = doc.pmid
unified_example["document_id"] = doc.pmid
unified_example["passages"] = [
{
"id": doc.pmid + "_title",
"type": "title",
"text": [doc.title],
"offsets": [[0, len(doc.title)]],
},
{
"id": doc.pmid + "_abstract",
"type": "abstract",
"text": [doc.abstract],
"offsets": [
[
# +1 assumes the title and abstract will be joined by a space.
len(doc.title) + 1,
len(doc.title) + 1 + len(doc.abstract),
]
],
},
]
unified_entities = []
for i, entity in enumerate(doc.annotations):
# We need a unique identifier for this entity, so build it from the document id and entity id
unified_entity_id = "_".join([doc.pmid, entity.id, str(i)])
# The user can provide a callable that returns the database name.
db_name = "OMIM" if "OMIM" in entity.id else "MESH"
normalized = []
for x in entity.id.split("|"):
if x.startswith("OMIM") or x.startswith("omim"):
normalized.append(
{"db_name": "OMIM", "db_id": x.strip().split(":")[-1]}
)
elif "+" in x:
normalized.extend(
[
{
"db_name": "MESH",
"db_id": y.split(":")[-1].strip(),
}
for y in x.split("+")
]
)
else:
normalized.append(
{"db_name": "MESH", "db_id": x.split(":")[-1].strip()})
unified_entities.append(
{
"id": unified_entity_id,
"type": entity.type,
"text": [entity.text],
"offsets": [[entity.start, entity.end]],
"normalized": normalized,
}
)
unified_example["entities"] = unified_entities
unified_example["relations"] = []
unified_example["events"] = []
unified_example["coreferences"] = []
yield unified_example