# pip install bs4 syntok import os import random import datasets import numpy as np from bs4 import BeautifulSoup, ResultSet from syntok.tokenizer import Tokenizer tokenizer = Tokenizer() _CITATION = """\ @report{Magnini2021, \ author = {Bernardo Magnini and Begoña Altuna and Alberto Lavelli and Manuela Speranza \ and Roberto Zanoli and Fondazione Bruno Kessler}, \ keywords = {Clinical data,clinical enti-ties,corpus,multilingual,temporal information}, \ title = {The E3C Project: \ European Clinical Case Corpus El proyecto E3C: European Clinical Case Corpus}, \ url = {https://uts.nlm.nih.gov/uts/umls/home}, \ year = {2021}, \ } """ _DESCRIPTION = """\ E3C is a freely available multilingual corpus (English, French, Italian, Spanish, and Basque) \ of semantically annotated clinical narratives to allow for the linguistic analysis, benchmarking, \ and training of information extraction systems. It consists of two types of annotations: \ (i) clinical entities (e.g., pathologies), (ii) temporal information and factuality (e.g., events). \ Researchers can use the benchmark training and test splits of our corpus to develop and test \ their own models. """ _URL = "https://github.com/hltfbk/E3C-Corpus/archive/refs/tags/v2.0.0.zip" _LANGUAGES = ["English","Spanish","Basque","French","Italian"] class E3C(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ datasets.BuilderConfig(name=f"{lang}_clinical", version="1.0.0", description=f"The {lang} subset of the E3C corpus") for lang in _LANGUAGES ] BUILDER_CONFIGS += [ datasets.BuilderConfig(name=f"{lang}_temporal", version="1.0.0", description=f"The {lang} subset of the E3C corpus") for lang in _LANGUAGES ] DEFAULT_CONFIG_NAME = "French_clinical" def _info(self): if self.config.name == "default": self.config.name = self.DEFAULT_CONFIG_NAME if self.config.name.find("clinical") != -1: names = ["O","B-CLINENTITY","I-CLINENTITY"] elif self.config.name.find("temporal") != -1: names = ["O", "B-EVENT", "B-ACTOR", "B-BODYPART", "B-TIMEX3", "B-RML", "I-EVENT", "I-ACTOR", "I-BODYPART", "I-TIMEX3", "I-RML"] features = datasets.Features( { "id": datasets.Value("string"), "text": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=names, ), ), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, citation=_CITATION, supervised_keys=None, ) def _split_generators(self, dl_manager): data_dir = dl_manager.download_and_extract(_URL) print(data_dir) if self.config.name.find("clinical") != -1: print("clinical") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_clinical",""), "layer2"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_clinical",""), "layer2"), "split": "validation", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_clinical",""), "layer1"), "split": "test", }, ), ] elif self.config.name.find("temporal") != -1: print("temporal") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_temporal",""), "layer1"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_temporal",""), "layer1"), "split": "validation", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_temporal",""), "layer1"), "split": "test", }, ), ] @staticmethod def get_annotations(entities: ResultSet, text: str) -> list: return [[ int(entity.get("begin")), int(entity.get("end")), text[int(entity.get("begin")) : int(entity.get("end"))], ] for entity in entities] def get_clinical_annotations(self, entities: ResultSet, text: str) -> list: return [[ int(entity.get("begin")), int(entity.get("end")), text[int(entity.get("begin")) : int(entity.get("end"))], entity.get("entityID"), ] for entity in entities] def get_parsed_data(self, filepath: str): for root, _, files in os.walk(filepath): for file in files: with open(f"{root}/{file}") as soup_file: soup = BeautifulSoup(soup_file, "xml") text = soup.find("cas:Sofa").get("sofaString") yield { "CLINENTITY": self.get_clinical_annotations(soup.find_all("custom:CLINENTITY"), text), "EVENT": self.get_annotations(soup.find_all("custom:EVENT"), text), "ACTOR": self.get_annotations(soup.find_all("custom:ACTOR"), text), "BODYPART": self.get_annotations(soup.find_all("custom:BODYPART"), text), "TIMEX3": self.get_annotations(soup.find_all("custom:TIMEX3"), text), "RML": self.get_annotations(soup.find_all("custom:RML"), text), "SENTENCE": self.get_annotations(soup.find_all("type4:Sentence"), text), "TOKENS": self.get_annotations(soup.find_all("type4:Token"), text), } def _generate_examples(self, filepath, split): all_res = [] key = 0 parsed_content = self.get_parsed_data(filepath) for content in parsed_content: for sentence in content["SENTENCE"]: tokens = [( token.offset + sentence[0], token.offset + sentence[0] + len(token.value), token.value, ) for token in list(tokenizer.tokenize(sentence[-1]))] filtered_tokens = list( filter( lambda token: token[0] >= sentence[0] and token[1] <= sentence[1], tokens, ) ) tokens_offsets = [ [token[0] - sentence[0], token[1] - sentence[0]] for token in filtered_tokens ] clinical_labels = ["O"] * len(filtered_tokens) clinical_cuid = ["CUI_LESS"] * len(filtered_tokens) temporal_information_labels = ["O"] * len(filtered_tokens) for entity_type in ["CLINENTITY","EVENT","ACTOR","BODYPART","TIMEX3","RML"]: if len(content[entity_type]) != 0: for entities in list(content[entity_type]): annotated_tokens = [ idx_token for idx_token, token in enumerate(filtered_tokens) if token[0] >= entities[0] and token[1] <= entities[1] ] for idx_token in annotated_tokens: if entity_type == "CLINENTITY": if idx_token == annotated_tokens[0]: clinical_labels[idx_token] = f"B-{entity_type}" else: clinical_labels[idx_token] = f"I-{entity_type}" clinical_cuid[idx_token] = entities[-1] else: if idx_token == annotated_tokens[0]: temporal_information_labels[idx_token] = f"B-{entity_type}" else: temporal_information_labels[idx_token] = f"I-{entity_type}" if self.config.name.find("clinical") != -1: _labels = clinical_labels elif self.config.name.find("temporal") != -1: _labels = temporal_information_labels all_res.append({ "id": key, "text": sentence[-1], "tokens": list(map(lambda token: token[2], filtered_tokens)), "ner_tags": _labels, }) key += 1 if self.config.name.find("clinical") != -1: if split != "test": ids = [r["id"] for r in all_res] random.seed(4) random.shuffle(ids) random.shuffle(ids) random.shuffle(ids) train, validation = np.split(ids, [int(len(ids)*0.8738)]) if split == "train": allowed_ids = list(train) elif split == "validation": allowed_ids = list(validation) for r in all_res: if r["id"] in allowed_ids: yield r["id"], r else: for r in all_res: yield r["id"], r elif self.config.name.find("temporal") != -1: 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