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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 = "pos_spec" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="pos", version="1.0.0", description="The CAS corpora - POS Speculation task"), |
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datasets.BuilderConfig(name="cls", version="1.0.0", description="The CAS corpora - CLS Negation / Speculation task"), |
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datasets.BuilderConfig(name="ner_spec", version="1.0.0", description="The CAS corpora - NER Speculation task"), |
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datasets.BuilderConfig(name="ner_neg", version="1.0.0", description="The CAS corpora - NER Negation task"), |
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] |
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def _info(self): |
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if self.config.name.find("pos") != -1: |
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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 = ['B-INT', 'B-PRO:DEM', 'B-VER:impf', 'B-VER:ppre', 'B-PRP:det', 'B-KON', 'B-VER:pper', 'B-PRP', 'B-PRO:IND', 'B-VER:simp', 'B-VER:con', 'B-SENT', 'B-VER:futu', 'B-PRO:PER', 'B-VER:infi', 'B-ADJ', 'B-NAM', 'B-NUM', 'B-PUN:cit', 'B-PRO:REL', 'B-VER:subi', 'B-ABR', 'B-NOM', 'B-VER:pres', 'B-DET:ART', 'B-VER:cond', 'B-VER:subp', 'B-DET:POS', 'B-ADV', 'B-SYM', 'B-PUN'], |
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)], |
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} |
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) |
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elif self.config.name.find("cls") != -1: |
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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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"label": datasets.features.ClassLabel( |
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names = ['negation_speculation', 'speculation', 'neutral', 'negation'], |
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), |
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} |
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) |
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elif self.config.name.find("ner") != -1: |
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if self.config.name.find("_spec") != -1: |
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names = ['O', 'B_xcope_inc', 'I_xcope_inc'] |
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elif self.config.name.find("_neg") != -1: |
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names = ['O', 'B_scope_neg', 'I_scope_neg'] |
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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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"ner_tags": [datasets.features.ClassLabel( |
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names = names, |
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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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subset = self.config.name.split("_")[-1] |
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unique_id_doc = [] |
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if self.config.name.find("ner") != -1: |
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docs = [f"CAS_{subset}.txt"] |
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else: |
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docs = ["CAS_neg.txt", "CAS_spec.txt"] |
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for file in docs: |
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filename = os.path.join(datadir, file) |
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if self.config.name.find("pos") != -1: |
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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(filename) as f: |
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for line in f.readlines(): |
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splitted = line.split("\t") |
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if len(splitted) < 5: |
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continue |
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id_doc, id_word, word, lemma, tag = splitted[0:5] |
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if len(splitted) >= 8: |
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tag = splitted[6] |
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if tag == "@card@": |
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print(splitted) |
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if word == "@card@": |
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print(splitted) |
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if lemma == "000" and tag == "@card@": |
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tag = "NUM" |
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word = "100 000" |
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lemma = "100 000" |
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elif lemma == "45" and tag == "@card@": |
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tag = "NUM" |
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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('B-'+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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if doc_id not in unique_id_doc: |
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all_res.append({ |
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"id": str(doc_id), |
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"document_id": 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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}) |
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unique_id_doc.append(doc_id) |
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elif self.config.name.find("ner") != -1: |
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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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ner_tags = [] |
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with open(filename) 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, _ = line.split("\t")[0:5] |
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tag = line.replace("\n","").split("\t")[-1] |
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if tag == "***" or tag == "_": |
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tag = "O" |
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elif tag == "I_xcope_inc_": |
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tag = "I_xcope_inc" |
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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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ner_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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"ner_tags": ner_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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ner_tags = [dic["ner_tags"][id] for id in indexes] |
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all_res.append({ |
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"id": key, |
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"document_id": doc_id, |
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"tokens": tokens, |
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"lemmas": text_lemmas, |
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"ner_tags": ner_tags, |
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}) |
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key += 1 |
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elif self.config.name.find("cls") != -1: |
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f_in = open(filename, "r") |
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conll = [ |
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[b.split("\t") for b in a.split("\n")] |
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for a in f_in.read().split("\n\n") |
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] |
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f_in.close() |
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classe = "negation" if filename.find("_neg") != -1 else "speculation" |
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for document in conll: |
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if document == [""]: |
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continue |
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identifier = document[0][0] |
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unique = list(set([w[-1] for w in document])) |
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tokens = [sent[2] for sent in document if len(sent) > 1] |
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if "***" in unique: |
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l = "neutral" |
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elif "_" in unique: |
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l = classe |
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if identifier in unique_id_doc and l == 'neutral': |
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continue |
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elif identifier in unique_id_doc and l != 'neutral': |
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index_l = unique_id_doc.index(identifier) |
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if all_res[index_l]["label"] != "neutral": |
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l = "negation_speculation" |
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all_res[index_l] = { |
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"id": str(identifier), |
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"document_id": identifier, |
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"tokens": tokens, |
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"label": l, |
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} |
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else: |
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all_res.append({ |
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"id": str(identifier), |
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"document_id": identifier, |
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"tokens": tokens, |
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"label": l, |
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}) |
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unique_id_doc.append(identifier) |
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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 |
