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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.
"""
To this end, we set up a challenge task through BioCreative V to automatically
extract CDRs from the literature. More specifically, we designed two challenge
tasks: disease named entity recognition (DNER) and chemical-induced disease
(CID) relation extraction. To assist system development and assessment, we
created a large annotated text corpus that consists of human annotations of
all chemicals, diseases and their interactions in 1,500 PubMed articles.
-- 'Overview of the BioCreative V Chemical Disease Relation (CDR) Task'
"""
import collections
import itertools
import os
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{DBLP:journals/biodb/LiSJSWLDMWL16,
author = {Jiao Li and
Yueping Sun and
Robin J. Johnson and
Daniela Sciaky and
Chih{-}Hsuan Wei and
Robert Leaman and
Allan Peter Davis and
Carolyn J. Mattingly and
Thomas C. Wiegers and
Zhiyong Lu},
title = {BioCreative {V} {CDR} task corpus: a resource for chemical disease
relation extraction},
journal = {Database J. Biol. Databases Curation},
volume = {2016},
year = {2016},
url = {https://doi.org/10.1093/database/baw068},
doi = {10.1093/database/baw068},
timestamp = {Thu, 13 Aug 2020 12:41:41 +0200},
biburl = {https://dblp.org/rec/journals/biodb/LiSJSWLDMWL16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DATASETNAME = "bc5cdr"
_DISPLAYNAME = "BC5CDR"
_DESCRIPTION = """\
The BioCreative V Chemical Disease Relation (CDR) dataset is a large annotated \
text corpus of human annotations of all chemicals, diseases and their \
interactions in 1,500 PubMed articles.
"""
_HOMEPAGE = "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-v/track-3-cdr/"
_LICENSE = 'Public Domain Mark 1.0'
_URLs = {
"source": "https://github.com/JHnlp/BioCreative-V-CDR-Corpus/raw/master/CDR_Data.zip",
"bigbio_kb": "https://github.com/JHnlp/BioCreative-V-CDR-Corpus/raw/master/CDR_Data.zip",
}
_SUPPORTED_TASKS = [
Tasks.NAMED_ENTITY_RECOGNITION,
Tasks.NAMED_ENTITY_DISAMBIGUATION,
Tasks.RELATION_EXTRACTION,
]
_SOURCE_VERSION = "01.05.16"
_BIGBIO_VERSION = "1.0.0"
class Bc5cdrDataset(datasets.GeneratorBasedBuilder):
"""
BioCreative V Chemical Disease Relation (CDR) Task.
"""
DEFAULT_CONFIG_NAME = "bc5cdr_source"
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="bc5cdr_source",
version=SOURCE_VERSION,
description="BC5CDR source schema",
schema="source",
subset_id="bc5cdr",
),
BigBioConfig(
name="bc5cdr_bigbio_kb",
version=BIGBIO_VERSION,
description="BC5CDR simplified BigBio schema",
schema="bigbio_kb",
subset_id="bc5cdr",
),
]
def _info(self):
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"),
}
],
}
],
"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,
supervised_keys=None,
homepage=_HOMEPAGE,
license=str(_LICENSE),
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
my_urls = _URLs[self.config.schema]
data_dir = dl_manager.download_and_extract(my_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(
data_dir, "CDR_Data/CDR.Corpus.v010516/CDR_TrainingSet.BioC.xml"
),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(
data_dir, "CDR_Data/CDR.Corpus.v010516/CDR_TestSet.BioC.xml"
),
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(
data_dir,
"CDR_Data/CDR.Corpus.v010516/CDR_DevelopmentSet.BioC.xml",
),
"split": "dev",
},
),
]
def _get_bioc_entity(self, span, doc_text, db_id_key="MESH"):
"""Parse BioC entity annotation.
Parameters
----------
span : BioCAnnotation
BioC entity annotation
doc_text : string
document text, required to construct text spans
db_id_key : str, optional
database name used for normalization, by default "MESH"
Returns
-------
dict
entity information
"""
# offsets = [(loc.offset, loc.offset + loc.length) for loc in span.locations]
# texts = [doc_text[i:j] for i, j in offsets]
offsets, texts = get_texts_and_offsets_from_bioc_ann(span)
db_ids = span.infons[db_id_key] if db_id_key else "-1"
# some entities are not linked and
# some entities are linked to multiple normalized ids
if db_ids == "-1":
db_ids_list = []
else:
db_ids_list = db_ids.split("|")
normalized = [{"db_name": db_id_key, "db_id": db_id} for db_id in db_ids_list]
return {
"id": span.id,
"offsets": offsets,
"text": texts,
"type": span.infons["type"],
"normalized": normalized,
}
def _get_relations(self, relations, entities):
"""
BC5CDR provides abstract-level annotations for entity-linked relation
pairs rather than materializing links between all surface form
mentions of relations. An example from train id=2670794, the relation
- (chemical, disease) (D014148, D004211)
is materialized as 6 mentions of entity pairs
- 2x ('tranexamic acid', 'intravascular coagulation')
- 4x ('AMCA', 'intravascular coagulation')
"""
# index entities by normalized id
index = collections.defaultdict(list)
for ent in entities:
for norm in ent["normalized"]:
index[norm["db_id"]].append(ent)
index = dict(index)
# transform doc-level relations to mention-level
rela_mentions = []
for rela in relations:
arg1 = rela.infons["Chemical"]
arg2 = rela.infons["Disease"]
# all mention pairs
all_pairs = itertools.product(index[arg1], index[arg2])
for a, b in all_pairs:
# create relations linked by entity ids
rela_mentions.append(
{
"id": None,
"type": rela.infons["relation"],
"arg1_id": a["id"],
"arg2_id": b["id"],
"normalized": [],
}
)
return rela_mentions
def _get_document_text(self, xdoc):
"""Build document text for unit testing entity span offsets."""
text = ""
for passage in xdoc.passages:
pad = passage.offset - len(text)
text += (" " * pad) + passage.text
return text
def _generate_examples(
self,
filepath,
split, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
):
"""Yields examples as (key, example) tuples."""
if self.config.schema == "source":
reader = biocxml.BioCXMLDocumentReader(str(filepath))
for uid, xdoc in enumerate(reader):
doc_text = self._get_document_text(xdoc)
yield uid, {
"passages": [
{
"document_id": xdoc.id,
"type": passage.infons["type"],
"text": passage.text,
"entities": [
self._get_bioc_entity(span, doc_text)
for span in passage.annotations
],
"relations": [
{
"id": rel.id,
"type": rel.infons["relation"],
"arg1_id": rel.infons["Chemical"],
"arg2_id": rel.infons["Disease"],
}
for rel in xdoc.relations
],
}
for passage in xdoc.passages
]
}
elif self.config.schema == "bigbio_kb":
reader = biocxml.BioCXMLDocumentReader(str(filepath))
uid = 0 # global unique id
for i, xdoc in enumerate(reader):
data = {
"id": uid,
"document_id": xdoc.id,
"passages": [],
"entities": [],
"relations": [],
"events": [],
"coreferences": [],
}
uid += 1
doc_text = self._get_document_text(xdoc)
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, doc_text, db_id_key="MESH")
ent["id"] = uid # override BioC default id
data["entities"].append(ent)
uid += 1
# relations
relations = self._get_relations(xdoc.relations, data["entities"])
for rela in relations:
rela["id"] = uid # assign unique id
data["relations"].append(rela)
uid += 1
yield i, data
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