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import os
import datasets
import numpy as np
import pandas as pd
from .bigbiohub import text_features
from .bigbiohub import kb_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks
_LANGUAGES = ['French']
_PUBMED = False
_LOCAL = True
_CITATION = """\
@misc{dalloux, title={Datasets – Clément Dalloux}, url={http://clementdalloux.fr/?page_id=28}, journal={Clément Dalloux}, author={Dalloux, Clément}}
"""
_DATASETNAME = "essai"
_DISPLAYNAME = "ESSAI"
_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'
_URLS = {
"essai_source": "",
"essai_bigbio_text": "",
"essai_bigbio_kb": "",
}
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
class ESSAI(datasets.GeneratorBasedBuilder):
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
DEFAULT_CONFIG_NAME = "essai_source"
BUILDER_CONFIGS = [
BigBioConfig(
name="essai_source",
version=SOURCE_VERSION,
description="ESSAI source schema",
schema="source",
subset_id="essai",
),
BigBioConfig(
name="essai_bigbio_text",
version=BIGBIO_VERSION,
description="ESSAI simplified BigBio schema for negation/speculation classification",
schema="bigbio_text",
subset_id="essai",
),
BigBioConfig(
name="essai_bigbio_kb",
version=BIGBIO_VERSION,
description="ESSAI simplified BigBio schema for part-of-speech-tagging",
schema="bigbio_kb",
subset_id="essai",
),
]
def _info(self):
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"text": [datasets.Value("string")],
"lemmas": [datasets.Value("string")],
"POS_tags": [datasets.Value("string")],
"labels": [datasets.Value("string")],
}
)
elif self.config.schema == "bigbio_text":
features = text_features
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):
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},
),
]
def _generate_examples(self, datadir):
key = 0
for file in ["ESSAI_neg.txt", "ESSAI_spec.txt"]:
filepath = os.path.join(datadir, file)
label = "negation" if "neg" in file else "speculation"
id_docs = []
id_words = []
words = []
lemmas = []
POS_tags = []
with open(filepath) as f:
for line in f.readlines():
line_content = line.split("\t")
if len(line_content) > 1:
id_docs.append(line_content[0])
id_words.append(line_content[1])
words.append(line_content[2])
lemmas.append(line_content[3])
POS_tags.append(line_content[4])
dic = {
"id_docs": np.array(list(map(int, id_docs))),
"id_words": id_words,
"words": words,
"lemmas": lemmas,
"POS_tags": POS_tags,
}
if self.config.schema == "source":
for doc_id in set(dic["id_docs"]):
idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
text = [dic["words"][id] for id in idces]
text_lemmas = [dic["lemmas"][id] for id in idces]
POS_tags_ = [dic["POS_tags"][id] for id in idces]
yield key, {
"id": key,
"document_id": doc_id,
"text": text,
"lemmas": text_lemmas,
"POS_tags": POS_tags_,
"labels": [label],
}
key += 1
elif self.config.schema == "bigbio_text":
for doc_id in set(dic["id_docs"]):
idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
text = " ".join([dic["words"][id] for id in idces])
yield key, {
"id": key,
"document_id": doc_id,
"text": text,
"labels": [label],
}
key += 1
elif self.config.schema == "bigbio_kb":
for doc_id in set(dic["id_docs"]):
idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
text = [dic["words"][id] for id in idces]
POS_tags_ = [dic["POS_tags"][id] for id in idces]
data = {
"id": str(key),
"document_id": doc_id,
"passages": [],
"entities": [],
"relations": [],
"events": [],
"coreferences": [],
}
key += 1
data["passages"] = [
{
"id": str(key + i),
"type": "sentence",
"text": [text[i]],
"offsets": [[i, i + 1]],
}
for i in range(len(text))
]
key += len(text)
for i in range(len(text)):
entity = {
"id": key,
"type": "POS_tag",
"text": [POS_tags_[i]],
"offsets": [[i, i + 1]],
"normalized": [],
}
data["entities"].append(entity)
key += 1
yield key, data
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