| import os |
| import random |
|
|
| import datasets |
| import numpy as np |
|
|
| _CITATION = """\ |
| @inproceedings{grabar-etal-2018-cas, |
| title = {{CAS}: {F}rench Corpus with Clinical Cases}, |
| author = {Grabar, Natalia and Claveau, Vincent and Dalloux, Cl{\'e}ment}, |
| year = 2018, |
| month = oct, |
| booktitle = { |
| Proceedings of the Ninth International Workshop on Health Text Mining and |
| Information Analysis |
| }, |
| publisher = {Association for Computational Linguistics}, |
| address = {Brussels, Belgium}, |
| pages = {122--128}, |
| doi = {10.18653/v1/W18-5614}, |
| url = {https://aclanthology.org/W18-5614}, |
| abstract = { |
| Textual corpora are extremely important for various NLP applications as |
| they provide information necessary for creating, setting and testing these |
| applications and the corresponding tools. They are also crucial for |
| designing reliable methods and reproducible results. Yet, in some areas, |
| such as the medical area, due to confidentiality or to ethical reasons, it |
| is complicated and even impossible to access textual data representative of |
| those produced in these areas. We propose the CAS corpus built with |
| clinical cases, such as they are reported in the published scientific |
| literature in French. We describe this corpus, currently containing over |
| 397,000 word occurrences, and the existing linguistic and semantic |
| annotations. |
| } |
| } |
| """ |
|
|
| _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 CAS corpus |
| """ |
|
|
| _HOMEPAGE = "https://clementdalloux.fr/?page_id=28" |
|
|
| _LICENSE = 'Data User Agreement' |
|
|
| _URL = "data.zip" |
|
|
|
|
| class CAS(datasets.GeneratorBasedBuilder): |
|
|
| DEFAULT_CONFIG_NAME = "pos" |
|
|
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig(name="pos", version="1.0.0", |
| description="The CAS corpora - POS Speculation task"), |
|
|
| datasets.BuilderConfig(name="cls", version="1.0.0", |
| description="The CAS corpora - CLS Negation / Speculation task"), |
|
|
| datasets.BuilderConfig(name="ner_spec", version="1.0.0", |
| description="The CAS corpora - NER Speculation task"), |
| datasets.BuilderConfig(name="ner_neg", version="1.0.0", |
| description="The CAS 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-ABR', 'B-ADJ', 'B-ADV', 'B-DET:ART', 'B-DET:POS', 'B-INT', |
| 'B-KON', 'B-NAM', 'B-NOM', 'B-NUM', |
| 'B-PRO:DEM', 'B-PRO:IND', 'B-PRO:PER', |
| 'B-PRO:REL', 'B-PRP', 'B-PRP:det', 'B-PUN', 'B-PUN:cit', |
| 'B-SENT', 'B-SYM', 'B-VER:con', 'B-VER:cond', 'B-VER:futu', |
| 'B-VER:impf', 'B-VER:infi', 'B-VER:pper', 'B-VER:ppre', |
| 'B-VER:pres', 'B-VER:simp', 'B-VER:subi', 'B-VER:subp' |
| ], |
| )], |
| } |
| ) |
|
|
| 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_xcope_inc', 'I_xcope_inc'] |
| elif self.config.name.find("_neg") != -1: |
| names = ['O', '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, |
| )], |
| } |
| ) |
|
|
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| supervised_keys=None, |
| homepage=_HOMEPAGE, |
| license=str(_LICENSE), |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
|
|
| data_dir = dl_manager.download_and_extract(_URL).rstrip("/") |
|
|
| 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"CAS_{subset}.txt"] |
| else: |
| docs = ["CAS_neg.txt", "CAS_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" |
|
|
| |
| |
|
|
| id_docs.append(id_doc) |
| id_words.append(id_word) |
| words.append(word) |
| lemmas.append(lemma) |
| POS_tags.append(f'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": tokens, |
| "lemmas": text_lemmas, |
| "pos_tags": pos_tags, |
| }) |
| unique_id_doc.append(doc_id) |
|
|
| |
|
|
| 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 == "I_xcope_inc_": |
| tag = "I_xcope_inc" |
| |
| |
| |
| |
|
|
| 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": tokens, |
| "lemmas": text_lemmas, |
| "ner_tags": ner_tags, |
| }) |
|
|
| 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": tokens, |
| "label": l, |
| } |
|
|
| else: |
|
|
| all_res.append({ |
| "id": str(identifier), |
| "document_id": identifier, |
| "tokens": 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 |
|
|
