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import os
import random
import requests
import datasets
import numpy as np
_CITATION = """\
@misc{
dalloux,
title={Datasets – Clément Dalloux},
url={http://clementdalloux.fr/?page_id=28},
journal={Clément Dalloux},
author={Dalloux, Clément}
}
"""
_DESCRIPTION = """\
We manually annotated two corpora from the biomedical field. The ESSAI corpus \
contains clinical trial protocols in French. They were mainly obtained from the \
National Cancer Institute The typical protocol consists of two parts: the \
summary of the trial, which indicates the purpose of the trial and the methods \
applied; and a detailed description of the trial with the inclusion and \
exclusion criteria. The CAS corpus contains clinical cases published in \
scientific literature and training material. They are published in different \
journals from French-speaking countries (France, Belgium, Switzerland, Canada, \
African countries, tropical countries) and are related to various medical \
specialties (cardiology, urology, oncology, obstetrics, pulmonology, \
gastro-enterology). The purpose of clinical cases is to describe clinical \
situations of patients. Hence, their content is close to the content of clinical \
narratives (description of diagnoses, treatments or procedures, evolution, \
family history, expected audience, etc.). In clinical cases, the negation is \
frequently used for describing the patient signs, symptoms, and diagnosis. \
Speculation is present as well but less frequently.
This version only contain the annotated ESSAI corpus
"""
_HOMEPAGE = "https://clementdalloux.fr/?page_id=28"
_LICENSE = 'Data User Agreement'
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 ESSAI(datasets.GeneratorBasedBuilder):
DEFAULT_CONFIG_NAME = "pos_spec"
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="pos", version="1.0.0", description="The ESSAI corpora - POS Speculation task"),
datasets.BuilderConfig(name="cls", version="1.0.0", description="The ESSAI corpora - CLS Negation / Speculation task"),
datasets.BuilderConfig(name="ner_spec", version="1.0.0", description="The ESSAI corpora - NER Speculation task"),
datasets.BuilderConfig(name="ner_neg", version="1.0.0", description="The ESSAI corpora - NER Negation task"),
]
def _info(self):
if self.config.name.find("pos") != -1:
features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"tokens": [datasets.Value("string")],
"lemmas": [datasets.Value("string")],
"pos_tags": [datasets.features.ClassLabel(
names = ['B-INT', 'B-PRO:POS', 'B-PRP', 'B-SENT', 'B-PRO', 'B-ABR', 'B-VER:pres', 'B-KON', 'B-SYM', 'B-DET:POS', 'B-VER:', 'B-PRO:IND', 'B-NAM', 'B-ADV', 'B-PRO:DEM', 'B-NN', 'B-PRO:PER', 'B-VER:pper', 'B-VER:ppre', 'B-PUN', 'B-VER:simp', 'B-PREF', 'B-NUM', 'B-VER:futu', 'B-NOM', 'B-VER:impf', 'B-VER:subp', 'B-VER:infi', 'B-DET:ART', 'B-PUN:cit', 'B-ADJ', 'B-PRP:det', 'B-PRO:REL', 'B-VER:cond', 'B-VER:subi'],
)],
"is_oov": datasets.Sequence(
datasets.features.ClassLabel(
names=['is_not_oov', 'is_oov'],
),
),
}
)
elif self.config.name.find("cls") != -1:
features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"tokens": [datasets.Value("string")],
"label": datasets.features.ClassLabel(
names = ['negation_speculation', 'negation', 'neutral', 'speculation'],
),
}
)
elif self.config.name.find("ner") != -1:
if self.config.name.find("_spec") != -1:
names = ['O', 'B_cue_spec', 'B_scope_spec', 'I_scope_spec']
elif self.config.name.find("_neg") != -1:
names = ['O', 'B_cue_neg', 'B_scope_neg', 'I_scope_neg']
features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"tokens": [datasets.Value("string")],
"lemmas": [datasets.Value("string")],
"ner_tags": [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,
supervised_keys=None,
homepage=_HOMEPAGE,
license=str(_LICENSE),
citation=_CITATION,
)
def _split_generators(self, dl_manager):
if self.config.data_dir is None:
raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.")
else:
data_dir = self.config.data_dir
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datadir": data_dir,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"datadir": data_dir,
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datadir": data_dir,
"split": "test",
},
),
]
def _generate_examples(self, datadir, split):
all_res = []
key = 0
subset = self.config.name.split("_")[-1]
unique_id_doc = []
if self.config.name.find("ner") != -1:
docs = [f"ESSAI_{subset}.txt"]
else:
docs = ["ESSAI_neg.txt", "ESSAI_spec.txt"]
for file in docs:
filename = os.path.join(datadir, file)
if self.config.name.find("pos") != -1:
id_docs = []
id_words = []
words = []
lemmas = []
POS_tags = []
with open(filename) as f:
for line in f.readlines():
splitted = line.split("\t")
if len(splitted) < 5:
continue
id_doc, id_word, word, lemma, tag = splitted[0:5]
if len(splitted) >= 8:
tag = splitted[6]
if tag == "@card@":
print(splitted)
if word == "@card@":
print(splitted)
if lemma == "000" and tag == "@card@":
tag = "NUM"
word = "100 000"
lemma = "100 000"
elif lemma == "45" and tag == "@card@":
tag = "NUM"
# if id_doc in id_docs:
# continue
id_docs.append(id_doc)
id_words.append(id_word)
words.append(word)
lemmas.append(lemma)
POS_tags.append('B-'+tag)
dic = {
"id_docs": np.array(list(map(int, id_docs))),
"id_words": id_words,
"words": words,
"lemmas": lemmas,
"POS_tags": POS_tags,
}
for doc_id in set(dic["id_docs"]):
indexes = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
tokens = [dic["words"][id] for id in indexes]
text_lemmas = [dic["lemmas"][id] for id in indexes]
pos_tags = [dic["POS_tags"][id] for id in indexes]
if doc_id not in unique_id_doc:
all_res.append({
"id": str(doc_id),
"document_id": doc_id,
"tokens": [tok.lower() for tok in tokens],
"lemmas": text_lemmas,
"pos_tags": pos_tags,
"is_oov": [_VOCAB.find(tt.lower()) for tt in tokens],
})
unique_id_doc.append(doc_id)
# key += 1
elif self.config.name.find("ner") != -1:
id_docs = []
id_words = []
words = []
lemmas = []
ner_tags = []
with open(filename) as f:
for line in f.readlines():
if len(line.split("\t")) < 5:
continue
id_doc, id_word, word, lemma, _ = line.split("\t")[0:5]
tag = line.replace("\n","").split("\t")[-1]
if tag == "***" or tag == "_":
tag = "O"
elif tag == "v":
tag = "I_scope_spec"
elif tag == "z":
tag = "O"
elif tag == "I_scope_spec_":
tag = "I_scope_spec"
id_docs.append(id_doc)
id_words.append(id_word)
words.append(word)
lemmas.append(lemma)
ner_tags.append(tag)
dic = {
"id_docs": np.array(list(map(int, id_docs))),
"id_words": id_words,
"words": words,
"lemmas": lemmas,
"ner_tags": ner_tags,
}
for doc_id in set(dic["id_docs"]):
indexes = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
tokens = [dic["words"][id] for id in indexes]
text_lemmas = [dic["lemmas"][id] for id in indexes]
ner_tags = [dic["ner_tags"][id] for id in indexes]
all_res.append({
"id": key,
"document_id": doc_id,
"tokens": [tok.lower() for tok in tokens],
"lemmas": text_lemmas,
"ner_tags": ner_tags,
"is_oov": [_VOCAB.find(tt.lower()) for tt in tokens],
})
key += 1
elif self.config.name.find("cls") != -1:
f_in = open(filename, "r")
conll = [
[b.split("\t") for b in a.split("\n")]
for a in f_in.read().split("\n\n")
]
f_in.close()
classe = "negation" if filename.find("_neg") != -1 else "speculation"
for document in conll:
if document == [""]:
continue
identifier = document[0][0]
unique = list(set([w[-1] for w in document]))
tokens = [sent[2] for sent in document if len(sent) > 1]
if "***" in unique:
l = "neutral"
elif "_" in unique:
l = classe
if identifier in unique_id_doc and l == 'neutral':
continue
elif identifier in unique_id_doc and l != 'neutral':
index_l = unique_id_doc.index(identifier)
if all_res[index_l]["label"] != "neutral":
l = "negation_speculation"
all_res[index_l] = {
"id": str(identifier),
"document_id": identifier,
"tokens": [tok.lower() for tok in tokens],
"label": l,
}
else:
all_res.append({
"id": str(identifier),
"document_id": identifier,
"tokens": [tok.lower() for tok in tokens],
"label": l,
})
unique_id_doc.append(identifier)
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 |