Create CAS.py
Browse files
CAS.py
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import os
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import random
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import datasets
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import numpy as np
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_CITATION = """\
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@inproceedings{grabar-etal-2018-cas,
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title = {{CAS}: {F}rench Corpus with Clinical Cases},
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author = {Grabar, Natalia and Claveau, Vincent and Dalloux, Cl{\'e}ment},
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year = 2018,
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month = oct,
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booktitle = {
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Proceedings of the Ninth International Workshop on Health Text Mining and
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Information Analysis
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},
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publisher = {Association for Computational Linguistics},
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address = {Brussels, Belgium},
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pages = {122--128},
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doi = {10.18653/v1/W18-5614},
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url = {https://aclanthology.org/W18-5614},
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abstract = {
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Textual corpora are extremely important for various NLP applications as
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they provide information necessary for creating, setting and testing these
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applications and the corresponding tools. They are also crucial for
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designing reliable methods and reproducible results. Yet, in some areas,
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such as the medical area, due to confidentiality or to ethical reasons, it
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is complicated and even impossible to access textual data representative of
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those produced in these areas. We propose the CAS corpus built with
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clinical cases, such as they are reported in the published scientific
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literature in French. We describe this corpus, currently containing over
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397,000 word occurrences, and the existing linguistic and semantic
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annotations.
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}
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}
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"""
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_DESCRIPTION = """\
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We manually annotated two corpora from the biomedical field. The ESSAI corpus \
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contains clinical trial protocols in French. They were mainly obtained from the \
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National Cancer Institute The typical protocol consists of two parts: the \
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summary of the trial, which indicates the purpose of the trial and the methods \
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applied; and a detailed description of the trial with the inclusion and \
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exclusion criteria. The CAS corpus contains clinical cases published in \
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scientific literature and training material. They are published in different \
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journals from French-speaking countries (France, Belgium, Switzerland, Canada, \
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African countries, tropical countries) and are related to various medical \
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specialties (cardiology, urology, oncology, obstetrics, pulmonology, \
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gastro-enterology). The purpose of clinical cases is to describe clinical \
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situations of patients. Hence, their content is close to the content of clinical \
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narratives (description of diagnoses, treatments or procedures, evolution, \
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family history, expected audience, etc.). In clinical cases, the negation is \
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frequently used for describing the patient signs, symptoms, and diagnosis. \
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Speculation is present as well but less frequently.
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This version only contain the annotated CAS corpus
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"""
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_HOMEPAGE = "https://clementdalloux.fr/?page_id=28"
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_LICENSE = 'Data User Agreement'
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class CAS(datasets.GeneratorBasedBuilder):
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DEFAULT_CONFIG_NAME = "source"
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="source", version="1.0.0", description="The CAS corpora"),
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]
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def _info(self):
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features = datasets.Features(
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{
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"id": datasets.Value("string"),
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"document_id": datasets.Value("string"),
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"tokens": [datasets.Value("string")],
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"lemmas": [datasets.Value("string")],
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"pos_tags": [datasets.features.ClassLabel(
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names = ['VER:ppre', 'VER:infi', 'VER:impf', 'VER:simp', 'PUN', 'DET:POS', 'ADV', 'DET:ART', 'PRO:DEM', 'INT', 'VER:futu', 'VER:subp', 'VER:cond', 'VER:pper', 'KON', 'NAM', 'PRO:IND', 'VER:con', 'PRP', 'SYM', 'SENT', 'PUN:cit', 'VER:pres', 'PRP:det', 'PRO:REL', 'PRO:PER', 'VER:subi', 'ADJ', 'NUM', 'NOM', 'ABR'],
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)],
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"label": datasets.features.ClassLabel(
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names = ['negation', 'speculation'],
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),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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supervised_keys=None,
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homepage=_HOMEPAGE,
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license=str(_LICENSE),
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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if self.config.data_dir is None:
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raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.")
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else:
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data_dir = self.config.data_dir
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"datadir": data_dir,
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"split": "train",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"datadir": data_dir,
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"split": "validation",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"datadir": data_dir,
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"split": "test",
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},
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),
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]
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def _generate_examples(self, datadir, split):
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all_res = []
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key = 0
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for file in ["CAS_neg.txt", "CAS_spec.txt"]:
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label = "negation" if "neg" in file else "speculation"
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id_docs = []
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id_words = []
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words = []
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lemmas = []
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POS_tags = []
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with open(os.path.join(datadir, file)) as f:
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for line in f.readlines():
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if len(line.split("\t")) < 5:
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continue
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id_doc, id_word, word, lemma, tag = line.split("\t")[0:5]
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id_docs.append(id_doc)
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id_words.append(id_word)
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words.append(word)
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lemmas.append(lemma)
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POS_tags.append(tag)
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dic = {
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"id_docs": np.array(list(map(int, id_docs))),
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"id_words": id_words,
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"words": words,
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"lemmas": lemmas,
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"POS_tags": POS_tags,
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}
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for doc_id in set(dic["id_docs"]):
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indexes = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
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tokens = [dic["words"][id] for id in indexes]
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text_lemmas = [dic["lemmas"][id] for id in indexes]
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pos_tags = [dic["POS_tags"][id] for id in indexes]
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all_res.append({
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"id": str(key),
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"document_id": str(doc_id),
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"tokens": tokens,
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"lemmas": text_lemmas,
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"pos_tags": pos_tags,
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"label": label,
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})
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key += 1
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ids = [r["id"] for r in all_res]
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random.seed(4)
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random.shuffle(ids)
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random.shuffle(ids)
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random.shuffle(ids)
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train, validation, test = np.split(ids, [int(len(ids)*0.70), int(len(ids)*0.80)])
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if split == "train":
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allowed_ids = list(train)
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elif split == "validation":
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allowed_ids = list(validation)
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elif split == "test":
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allowed_ids = list(test)
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for r in all_res:
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if r["id"] in allowed_ids:
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yield r["id"], r
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