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