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import os
from typing import Iterator
import datasets
from bs4 import BeautifulSoup, ResultSet
from datasets import DownloadManager
from syntok.tokenizer import Tokenizer
tok = Tokenizer()
_CITATION = """\
@report{Magnini2021,
author = {Bernardo Magnini and Begoña Altuna and Alberto Lavelli and Manuela Speranza
and Roberto Zanoli and Fondazione Bruno Kessler},
keywords = {Clinical data,clinical enti-ties,corpus,multilingual,temporal information},
title = {The E3C Project:
European Clinical Case Corpus El proyecto E3C: European Clinical Case Corpus},
url = {https://uts.nlm.nih.gov/uts/umls/home},
year = {2021},
}
"""
_DESCRIPTION = """\
The European Clinical Case Corpus (E3C) project aims at collecting and \
annotating a large corpus of clinical documents in five European languages (Spanish, \
Basque, English, French and Italian), which will be freely distributed. Annotations \
include temporal information, to allow temporal reasoning on chronologies, and \
information about clinical entities based on medical taxonomies, to be used for semantic reasoning.
"""
_URL = "https://github.com/hltfbk/E3C-Corpus/archive/refs/tags/v2.0.0.zip"
class E3CConfig(datasets.BuilderConfig):
"""BuilderConfig for E3C."""
def __init__(self, **kwargs):
"""BuilderConfig for E3C.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(E3CConfig, self).__init__(**kwargs)
class E3C(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
E3CConfig(
name="e3c",
version=VERSION,
description="this is an implementation of the E3C dataset",
),
]
def _info(self):
"""This method specifies the DatasetInfo which contains information and typings."""
features = datasets.Features(
{
"text": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"tokens_offsets": datasets.Sequence(datasets.Sequence(datasets.Value("int32"))),
"clinical_entity_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-CLINENTITY",
"I-CLINENTITY",
],
),
),
"clinical_entity_cuid": datasets.Sequence(
datasets.Value("string"),
),
"temporal_information_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-EVENT",
"B-ACTOR",
"B-BODYPART",
"B-TIMEX3",
"B-RML",
"I-EVENT",
"I-ACTOR",
"I-BODYPART",
"I-TIMEX3",
"I-RML",
],
),
),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
citation=_CITATION,
supervised_keys=None,
)
def _split_generators(self, dl_manager: DownloadManager) -> list[datasets.SplitGenerator]:
"""Returns SplitGenerators who contains all the difference splits of the dataset.
Each language has its own split and each split has 3 different layers (sub-split):
- layer 1: full manual annotation of clinical entities, temporal information and
factuality, for benchmarking and linguistic analysis.
- layer 2: semi-automatic annotation of clinical entities
- layer 3: non-annotated documents
Args:
dl_manager: A `datasets.utils.DownloadManager` that can be used to download and
extract URLs.
Returns:
A list of `datasets.SplitGenerator`. Contains all subsets of the dataset depending on
the language and the layer.
"""
url = _URL
data_dir = dl_manager.download_and_extract(url)
return [
datasets.SplitGenerator(
name="en.layer1",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_annotation",
"English",
"layer1",
),
},
),
datasets.SplitGenerator(
name="en.layer2",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_annotation",
"English",
"layer2",
),
},
),
datasets.SplitGenerator(
name="en.layer2.validation",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_validation",
"English",
"layer2",
),
},
),
datasets.SplitGenerator(
name="es.layer1",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_annotation",
"Spanish",
"layer1",
),
},
),
datasets.SplitGenerator(
name="es.layer2",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_annotation",
"Spanish",
"layer2",
),
},
),
datasets.SplitGenerator(
name="es.layer2.validation",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_validation",
"Spanish",
"layer2",
),
},
),
datasets.SplitGenerator(
name="eu.layer1",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_annotation",
"Basque",
"layer1",
),
},
),
datasets.SplitGenerator(
name="eu.layer2",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_annotation",
"Basque",
"layer2",
),
},
),
datasets.SplitGenerator(
name="eu.layer2.validation",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_validation",
"Basque",
"layer2",
),
},
),
datasets.SplitGenerator(
name="fr.layer1",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_annotation",
"French",
"layer1",
),
},
),
datasets.SplitGenerator(
name="fr.layer2",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_annotation",
"French",
"layer2",
),
},
),
datasets.SplitGenerator(
name="fr.layer2.validation",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_validation",
"French",
"layer2",
),
},
),
datasets.SplitGenerator(
name="it.layer1",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_annotation",
"Italian",
"layer1",
),
},
),
datasets.SplitGenerator(
name="it.layer2",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_annotation",
"Italian",
"layer2",
),
},
),
datasets.SplitGenerator(
name="it.layer2.validation",
gen_kwargs={
"filepath": os.path.join(
data_dir,
"E3C-Corpus-2.0.0/data_validation",
"Italian",
"layer2",
),
},
),
]
@staticmethod
def get_annotations(entities: ResultSet, text: str) -> list:
"""Extract the offset, the text and the type of the entity.
Args:
entities: The entities to extract.
text: The text of the document.
Returns:
A list of list containing the offset, the text and the type of the entity.
"""
return [
[
int(entity.get("begin")),
int(entity.get("end")),
text[int(entity.get("begin")) : int(entity.get("end"))],
]
for entity in entities
]
def get_clinical_annotations(self, entities: ResultSet, text: str) -> list:
"""Extract the offset, the text and the type of the entity.
Args:
entities: The entities to extract.
text: The text of the document.
Returns:
A list of list containing the offset, the text and the type of the entity.
"""
return [
[
int(entity.get("begin")),
int(entity.get("end")),
text[int(entity.get("begin")) : int(entity.get("end"))],
entity.get("entityID"),
]
for entity in entities
]
def get_parsed_data(self, filepath: str):
"""Parse the data from the E3C dataset and store it in a dictionary.
Iterate over the files in the dataset and parse for each file the following entities:
- CLINENTITY
- EVENT
- ACTOR
- BODYPART
- TIMEX3
- RML
for each entity, we extract the offset, the text and the type of the entity.
Args:
filepath: The path to the folder containing the files to parse.
"""
for root, _, files in os.walk(filepath):
for file in files:
with open(f"{root}/{file}") as soup_file:
soup = BeautifulSoup(soup_file, "xml")
text = soup.find("cas:Sofa").get("sofaString")
yield {
"CLINENTITY": self.get_clinical_annotations(
soup.find_all("custom:CLINENTITY"), text
),
"EVENT": self.get_annotations(soup.find_all("custom:EVENT"), text),
"ACTOR": self.get_annotations(soup.find_all("custom:ACTOR"), text),
"BODYPART": self.get_annotations(soup.find_all("custom:BODYPART"), text),
"TIMEX3": self.get_annotations(soup.find_all("custom:TIMEX3"), text),
"RML": self.get_annotations(soup.find_all("custom:RML"), text),
"SENTENCE": self.get_annotations(soup.find_all("type4:Sentence"), text),
"TOKENS": self.get_annotations(soup.find_all("type4:Token"), text),
}
def _generate_examples(self, filepath) -> Iterator:
"""Yields examples as (key, example) tuples.
Args:
filepath: The path to the folder containing the files to parse.
Yields:
an example containing four fields: the text, the annotations, the tokens offsets and
the sentences.
"""
guid = 0
for content in self.get_parsed_data(filepath):
for sentence in content["SENTENCE"]:
tokens = [
(
token.offset + sentence[0],
token.offset + sentence[0] + len(token.value),
token.value,
)
for token in list(tok.tokenize(sentence[-1]))
]
filtered_tokens = list(
filter(
lambda token: token[0] >= sentence[0] and token[1] <= sentence[1],
tokens,
)
)
tokens_offsets = [
[token[0] - sentence[0], token[1] - sentence[0]] for token in filtered_tokens
]
clinical_labels = ["O"] * len(filtered_tokens)
clinical_cuid = ["CUI_LESS"] * len(filtered_tokens)
temporal_information_labels = ["O"] * len(filtered_tokens)
for entity_type in [
"CLINENTITY",
"EVENT",
"ACTOR",
"BODYPART",
"TIMEX3",
"RML",
]:
if len(content[entity_type]) != 0:
for entities in list(
content[entity_type],
):
annotated_tokens = [
idx_token
for idx_token, token in enumerate(filtered_tokens)
if token[0] >= entities[0] and token[1] <= entities[1]
]
for idx_token in annotated_tokens:
if entity_type == "CLINENTITY":
if idx_token == annotated_tokens[0]:
clinical_labels[idx_token] = f"B-{entity_type}"
else:
clinical_labels[idx_token] = f"I-{entity_type}"
clinical_cuid[idx_token] = entities[-1]
else:
if idx_token == annotated_tokens[0]:
temporal_information_labels[idx_token] = f"B-{entity_type}"
else:
temporal_information_labels[idx_token] = f"I-{entity_type}"
yield guid, {
"text": sentence[-1],
"tokens": list(map(lambda token: token[2], filtered_tokens)),
"clinical_entity_tags": clinical_labels,
"clinical_entity_cuid": clinical_cuid,
"temporal_information_tags": temporal_information_labels,
"tokens_offsets": tokens_offsets,
}
guid += 1
if __name__ == "__main__":
builder = E3C()
builder.download_and_prepare() |