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