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# pip install bs4 syntok
import os
import random
import requests
import datasets
import numpy as np
from bs4 import BeautifulSoup, ResultSet
from syntok.tokenizer import Tokenizer
tokenizer = 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 = """\
E3C is a freely available multilingual corpus (English, French, Italian, Spanish, and Basque) \
of semantically annotated clinical narratives to allow for the linguistic analysis, benchmarking, \
and training of information extraction systems. It consists of two types of annotations: \
(i) clinical entities (e.g., pathologies), (ii) temporal information and factuality (e.g., events). \
Researchers can use the benchmark training and test splits of our corpus to develop and test \
their own models.
"""
_URL = "https://github.com/hltfbk/E3C-Corpus/archive/refs/tags/v2.0.0.zip"
_LANGUAGES = ["English","Spanish","Basque","French","Italian"]
class StringIndex:
def __init__(self, vocab):
self.vocab_struct = {}
print("Start building the index!")
for t in vocab:
if len(t) == 0:
continue
# Index terms by their first letter and length
key = (t[0], len(t))
if (key in self.vocab_struct) == False:
self.vocab_struct[key] = []
self.vocab_struct[key].append(t)
print("Finished building the index!")
def find(self, t):
key = (t[0], len(t))
if (key in self.vocab_struct) == False:
return "is_oov"
return "is_not_oov" if t in self.vocab_struct[key] else "is_oov"
_VOCAB = StringIndex(vocab=requests.get("https://huggingface.co/datasets/BioMedTok/vocabulary_nachos_lowercased/resolve/main/vocabulary_nachos_lowercased.txt").text.split("\n"))
class E3C(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(name=f"{lang}_clinical", version="1.0.0", description=f"The {lang} subset of the E3C corpus") for lang in _LANGUAGES
]
BUILDER_CONFIGS += [
datasets.BuilderConfig(name=f"{lang}_temporal", version="1.0.0", description=f"The {lang} subset of the E3C corpus") for lang in _LANGUAGES
]
DEFAULT_CONFIG_NAME = "French_clinical"
def _info(self):
if self.config.name == "default":
self.config.name = self.DEFAULT_CONFIG_NAME
if self.config.name.find("clinical") != -1:
names = ["O","B-CLINENTITY","I-CLINENTITY"]
elif self.config.name.find("temporal") != -1:
names = ["O", "B-EVENT", "B-ACTOR", "B-BODYPART", "B-TIMEX3", "B-RML", "I-EVENT", "I-ACTOR", "I-BODYPART", "I-TIMEX3", "I-RML"]
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=names,
),
),
"is_oov": datasets.Sequence(
datasets.features.ClassLabel(
names=['is_not_oov', 'is_oov'],
),
),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
citation=_CITATION,
supervised_keys=None,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URL)
print(data_dir)
if self.config.name.find("clinical") != -1:
print("clinical")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_clinical",""), "layer2"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_clinical",""), "layer2"),
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_clinical",""), "layer1"),
"split": "test",
},
),
]
elif self.config.name.find("temporal") != -1:
print("temporal")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_temporal",""), "layer1"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_temporal",""), "layer1"),
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_temporal",""), "layer1"),
"split": "test",
},
),
]
@staticmethod
def get_annotations(entities: ResultSet, text: str) -> list:
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:
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):
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, split):
all_res = []
key = 0
parsed_content = self.get_parsed_data(filepath)
for content in parsed_content:
for sentence in content["SENTENCE"]:
tokens = [(
token.offset + sentence[0],
token.offset + sentence[0] + len(token.value),
token.value,
) for token in list(tokenizer.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}"
if self.config.name.find("clinical") != -1:
_labels = clinical_labels
elif self.config.name.find("temporal") != -1:
_labels = temporal_information_labels
all_res.append({
"id": key,
"text": sentence[-1].lower(),
"tokens": [tok.lower() for tok in list(map(lambda token: token[2], filtered_tokens))],
"ner_tags": _labels,
"is_oov": [_VOCAB.find(tt.lower()) for tt in list(map(lambda token: token[2], filtered_tokens))],
})
key += 1
if self.config.name.find("clinical") != -1:
if split != "test":
ids = [r["id"] for r in all_res]
random.seed(4)
random.shuffle(ids)
random.shuffle(ids)
random.shuffle(ids)
train, validation = np.split(ids, [int(len(ids)*0.8738)])
if split == "train":
allowed_ids = list(train)
elif split == "validation":
allowed_ids = list(validation)
for r in all_res:
if r["id"] in allowed_ids:
yield r["id"], r
else:
for r in all_res:
yield r["id"], r
elif self.config.name.find("temporal") != -1:
ids = [r["id"] for r in all_res]
random.seed(4)
random.shuffle(ids)
random.shuffle(ids)
random.shuffle(ids)
train, validation, test = np.split(ids, [int(len(ids)*0.70), int(len(ids)*0.80)])
if split == "train":
allowed_ids = list(train)
elif split == "validation":
allowed_ids = list(validation)
elif split == "test":
allowed_ids = list(test)
for r in all_res:
if r["id"] in allowed_ids:
yield r["id"], r |