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import os
import datasets
from .bigbiohub import kb_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks
_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@article{VANMULLIGEN2012879,
title = {The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships},
journal = {Journal of Biomedical Informatics},
volume = {45},
number = {5},
pages = {879-884},
year = {2012},
note = {Text Mining and Natural Language Processing in Pharmacogenomics},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2012.04.004},
url = {https://www.sciencedirect.com/science/article/pii/S1532046412000573},
author = {Erik M. {van Mulligen} and Annie Fourrier-Reglat and David Gurwitz and Mariam Molokhia and Ainhoa Nieto and Gianluca Trifiro and Jan A. Kors and Laura I. Furlong},
keywords = {Text mining, Corpus development, Machine learning, Adverse drug reactions},
abstract = {Corpora with specific entities and relationships annotated are essential to train and evaluate text-mining systems that are developed to extract specific structured information from a large corpus. In this paper we describe an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface. The agreement figures achieved show that the inter-annotator agreement is much better than the agreement with the system provided annotations. The corpus has been annotated for drugs, disorders, genes and their inter-relationships. For each of the drug–disorder, drug–target, and target–disorder relations three experts have annotated a set of 100 abstracts. These annotated relationships will be used to train and evaluate text-mining software to capture these relationships in texts.}
}
"""
_DATASETNAME = "euadr"
_DISPLAYNAME = "EU-ADR"
_DESCRIPTION = """\
Corpora with specific entities and relationships annotated are essential to \
train and evaluate text-mining systems that are developed to extract specific \
structured information from a large corpus. In this paper we describe an \
approach where a named-entity recognition system produces a first annotation and \
annotators revise this annotation using a web-based interface. The agreement \
figures achieved show that the inter-annotator agreement is much better than the \
agreement with the system provided annotations. The corpus has been annotated \
for drugs, disorders, genes and their inter-relationships. For each of the \
drug-disorder, drug-target, and target-disorder relations three experts \
have annotated a set of 100 abstracts. These annotated relationships will be \
used to train and evaluate text-mining software to capture these relationships \
in texts.
"""
_HOMEPAGE = "https://www.sciencedirect.com/science/article/pii/S1532046412000573"
_LICENSE = 'License information unavailable'
_URL = "https://biosemantics.erasmusmc.nl/downloads/euadr.tgz"
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
class EUADR(datasets.GeneratorBasedBuilder):
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
DEFAULT_CONFIG_NAME = "euadr_bigbio_kb"
BUILDER_CONFIGS = [
BigBioConfig(
name="euadr_source",
version=SOURCE_VERSION,
description="EU-ADR source schema",
schema="source",
subset_id="euadr",
),
BigBioConfig(
name="euadr_bigbio_kb",
version=BIGBIO_VERSION,
description="EU-ADR simplified BigBio schema for named entity recognition and relation extraction",
schema="bigbio_kb",
subset_id="euadr",
),
]
def _info(self):
if self.config.schema == "source":
features = datasets.Features(
{
"pmid": datasets.Value("string"),
"title": datasets.Value("string"),
"abstract": datasets.Value("string"),
"annotations": datasets.Sequence(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):
urls = _URL
datapath = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"datapath": datapath, "dl_manager": dl_manager},
),
]
def _generate_examples(self, datapath, dl_manager):
def replace_html_special_chars(string):
# since we are getting the text as an HTML file, we need to replace
# special characters
for (i, r) in [
(""", '"'),
(""", '"'),
("'", "'"),
("'", "'"),
("&", "&"),
("&", "&"),
("<", "<"),
("<", "<"),
(">", ">"),
(">", ">"),
("'", "'"),
]:
string = string.replace(i, r)
return string
def suppr_blank(l_str):
r = []
for string in l_str:
if len(string) > 0:
r.append(string)
return r
folder_path = os.path.join(datapath, "euadr_corpus")
key = 0
if self.config.schema == "source":
for filename in sorted(os.listdir(folder_path)):
if "_" not in filename:
corpus_path = dl_manager.download_and_extract(
f"https://pubmed.ncbi.nlm.nih.gov/{filename[:-4]}/?format=pubmed"
)
with open(corpus_path, "r", encoding="latin") as f:
full_html = replace_html_special_chars(
("".join(f.readlines()))
.replace("\r\n", "")
.replace("\n", "")
)
abstract = " ".join(
suppr_blank(
full_html.split("AB -")[-1]
.split("FAU -")[0]
.split(" ")
)
)
title = " ".join(
suppr_blank(
full_html.split("TI -")[-1].split("PG")[0].split(" ")
)
)
full_text = " ".join([title, abstract])
with open(
os.path.join(folder_path, filename), "r", encoding="latin"
) as f:
lines = f.readlines()
yield key, {
"pmid": filename[:-4],
"title": title,
"abstract": abstract,
"annotations": lines,
}
key += 1
elif self.config.schema == "bigbio_kb":
for filename in sorted(os.listdir(folder_path)):
if "_" not in filename:
corpus_path = dl_manager.download_and_extract(
f"https://pubmed.ncbi.nlm.nih.gov/{filename[:-4]}/?format=pubmed"
)
with open(corpus_path, "r", encoding="latin") as f:
full_html = replace_html_special_chars(
("".join(f.readlines()))
.replace("\r\n", "")
.replace("\n", "")
)
abstract = " ".join(
suppr_blank(
full_html.split("AB -")[-1]
.split("FAU -")[0]
.split(" ")
)
)
title = " ".join(
suppr_blank(
full_html.split("TI -")[-1].split("PG")[0].split(" ")
)
)
full_text = " ".join([title, abstract])
with open(
os.path.join(folder_path, filename), "r", encoding="latin"
) as f:
lines = f.readlines()
data = {
"id": str(key),
"document_id": str(key),
"passages": [],
"entities": [],
"events": [],
"coreferences": [],
"relations": [],
}
key += 1
data["passages"].append(
{
"id": str(key),
"type": "title",
"text": [title],
"offsets": [[0, len(title)]],
}
)
key += 1
data["passages"].append(
{
"id": str(key),
"type": "abstract",
"text": [abstract],
"offsets": [
[len(title) + 1, len(title) + 1 + len(abstract)]
],
}
)
key += 1
for line in lines:
line_processed = line.split("\t")
if line_processed[2] == "relation":
data["entities"].append(
{
"id": str(key),
"offsets": [
[
int(line_processed[7].split(":")[0]),
int(line_processed[7].split(":")[1]),
]
],
"text": [
full_text[
int(
line_processed[7].split(":")[0]
) : int(line_processed[7].split(":")[1])
]
],
"type": "",
"normalized": [],
}
)
key += 1
data["entities"].append(
{
"id": str(key),
"offsets": [
[
int(line_processed[8].split(":")[0]),
int(line_processed[8].split(":")[1]),
]
],
"text": [
full_text[
int(
line_processed[8].split(":")[0]
) : int(line_processed[8].split(":")[1])
]
],
"type": "",
"normalized": [],
}
)
key += 1
data["relations"].append(
{
"id": str(key),
"type": line_processed[-1].split("\n")[0],
"arg1_id": str(key - 2),
"arg2_id": str(key - 1),
"normalized": [],
}
)
key += 1
elif line_processed[2] == "concept":
data["entities"].append(
{
"id": str(key),
"offsets": [
[
int(line_processed[4]),
int(line_processed[5]),
]
],
"text": [
full_text[
int(line_processed[4]) : int(
line_processed[5]
)
]
],
"type": line_processed[-1].split("\n")[0],
"normalized": [],
}
)
key += 1
yield key, data
key += 1
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