# coding=utf-8 # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ast from itertools import product from pathlib import Path from typing import Dict, List, Tuple import datasets from .bigbiohub import (BigBioConfig, Tasks, brat_parse_to_bigbio_kb, kb_features, parse_brat_file) _LANGUAGES = ["English", "French", "German", "Dutch", "Spanish"] _LOCAL = False _PUBMED = True _CITATION = """\ @article{10.1093/jamia/ocv037, author = {Kors, Jan A and Clematide, Simon and Akhondi, Saber A and van Mulligen, Erik M and Rebholz-Schuhmann, Dietrich}, title = "{A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC}", journal = {Journal of the American Medical Informatics Association}, volume = {22}, number = {5}, pages = {948-956}, year = {2015}, month = {05}, abstract = "{Objective To create a multilingual gold-standard corpus for biomedical concept recognition.Materials and methods We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and cross-language consistency checks were carried out to arrive at the final annotations.Results The number of final annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are similar to those between individual annotators and the gold standard. The automatically generated harmonized annotation set for each language performed equally well as the best annotator for that language.Discussion The use of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance of automatic annotation techniques.Conclusion To our knowledge, this is the first gold-standard corpus for biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety of semantic groups that are being covered, and the diversity of text genres that were annotated.}", issn = {1067-5027}, doi = {10.1093/jamia/ocv037}, url = {https://doi.org/10.1093/jamia/ocv037}, eprint = {https://academic.oup.com/jamia/article-pdf/22/5/948/34146393/ocv037.pdf}, } """ _DATASETNAME = "mantra_gsc" _DISPLAYNAME = "Mantra GSC" _DESCRIPTION = """\ We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. """ _HOMEPAGE = "https://github.com/mi-erasmusmc/Mantra-Gold-Standard-Corpus" _LICENSE = "GPL_3p0_ONLY" _URLS = { _DATASETNAME: "https://github.com/mi-erasmusmc/Mantra-Gold-Standard-Corpus/raw/main/Mantra-GSC-brat.zip", } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" _LANGUAGES_2 = { "es": "Spanish", "fr": "French", "de": "German", "nl": "Dutch", "en": "English", } _DATASET_TYPES = { "emea": "EMEA", "medline": "Medline", "patents": "Patents", } class MantraGSCDataset(datasets.GeneratorBasedBuilder): """Mantra Gold Standard Corpus (GSC) dataset.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [] for language, dataset_type in product(_LANGUAGES_2, _DATASET_TYPES): if dataset_type == "patents" and language in ["nl", "es"]: continue BUILDER_CONFIGS.append( BigBioConfig( name=f"mantra_gsc_{language}_{dataset_type}_source", version=SOURCE_VERSION, description=f"Mantra GSC {_LANGUAGES_2[language]} {_DATASET_TYPES[dataset_type]} source schema", schema="source", subset_id=f"mantra_gsc_{language}_{_DATASET_TYPES[dataset_type]}", ) ) BUILDER_CONFIGS.append( BigBioConfig( name=f"mantra_gsc_{language}_{dataset_type}_bigbio_kb", version=SOURCE_VERSION, description=f"Mantra GSC {_LANGUAGES_2[language]} {_DATASET_TYPES[dataset_type]} BigBio schema", schema="bigbio_kb", subset_id=f"mantra_gsc_{language}_{_DATASET_TYPES[dataset_type]}", ) ) DEFAULT_CONFIG_NAME = "mantra_gsc_en_medline_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "document_id": datasets.Value("string"), "text": datasets.Value("string"), "entities": [ { "entity_id": datasets.Value("string"), "type": datasets.Value("string"), "offsets": datasets.Sequence([datasets.Value("int32")]), "text": datasets.Sequence(datasets.Value("string")), "cui": datasets.Value("string"), "preferred_term": datasets.Value("string"), "semantic_type": datasets.Value("string"), "normalized": [ { "db_name": datasets.Value("string"), "db_id": datasets.Value("string"), } ], } ], } ) elif self.config.schema == "bigbio_kb": features = kb_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=str(_LICENSE), citation=_CITATION, ) def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" urls = _URLS[_DATASETNAME] data_dir = dl_manager.download_and_extract(urls) data_dir = Path(data_dir) / "Mantra-GSC" language, dataset_type = self.config.name.split("_")[2:4] return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "data_dir": data_dir, "language": language, "dataset_type": dataset_type, }, ), ] def _generate_examples(self, data_dir: Path, language: str, dataset_type: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" data_dir = data_dir / f"{_LANGUAGES_2[language]}" if dataset_type in ["patents", "emea"]: data_dir = data_dir / f"{_DATASET_TYPES[dataset_type]}_ec22-cui-best_man" else: # It is Medline now if language != "en": data_dir = data_dir / f"{_DATASET_TYPES[dataset_type]}_EN_{language.upper()}_ec22-cui-best_man" else: data_dir = [ data_dir / f"{_DATASET_TYPES[dataset_type]}_EN_{_lang.upper()}_ec22-cui-best_man" for _lang in _LANGUAGES_2 if _lang != "en" ] if not isinstance(data_dir, list): data_dir: List[Path] = [data_dir] raw_files = [raw_file for _dir in data_dir for raw_file in _dir.glob("*.txt")] if self.config.schema == "source": for i, raw_file in enumerate(raw_files): brat_example = parse_brat_file(raw_file, parse_notes=True) source_example = self._to_source_example(brat_example) yield i, source_example elif self.config.schema == "bigbio_kb": for i, raw_file in enumerate(raw_files): brat_example = parse_brat_file(raw_file, parse_notes=True) brat_to_bigbio_example = self._brat_to_bigbio_example(brat_example) kb_example = brat_parse_to_bigbio_kb(brat_to_bigbio_example) kb_example["id"] = i yield i, kb_example def _to_source_example(self, brat_example: Dict) -> Dict: source_example = { "document_id": brat_example["document_id"], "text": brat_example["text"], } source_example["entities"] = [] for entity_annotation, ann_notes in zip(brat_example["text_bound_annotations"], brat_example["notes"]): entity_ann = entity_annotation.copy() # Change id property name entity_ann["entity_id"] = entity_ann["id"] entity_ann.pop("id") # Get values from annotator notes assert entity_ann["entity_id"] == ann_notes["ref_id"] notes_values = ast.literal_eval(ann_notes["text"]) if len(notes_values) == 4: cui, preferred_term, semantic_type, semantic_group = notes_values else: preferred_term, semantic_type, semantic_group = notes_values cui = entity_ann["type"] entity_ann["cui"] = cui entity_ann["preferred_term"] = preferred_term entity_ann["semantic_type"] = semantic_type entity_ann["type"] = semantic_group entity_ann["normalized"] = [{"db_name": "UMLS", "db_id": cui}] # Add entity annotation to sample source_example["entities"].append(entity_ann) return source_example def _brat_to_bigbio_example(self, brat_example: Dict) -> Dict: kb_example = { "document_id": brat_example["document_id"], # "unit_id": unit_id, "text": brat_example["text"], } kb_example["text_bound_annotations"] = [] kb_example["normalizations"] = [] for entity_annotation, ann_notes in zip(brat_example["text_bound_annotations"], brat_example["notes"]): entity_ann = entity_annotation.copy() # Get values from annotator notes assert entity_ann["id"] == ann_notes["ref_id"] notes_values = ast.literal_eval(ann_notes["text"]) if len(notes_values) == 4: cui, _, _, semantic_group = notes_values else: _, _, semantic_group = notes_values cui = entity_ann["type"] entity_ann["type"] = semantic_group kb_example["text_bound_annotations"].append(entity_ann) kb_example["normalizations"].append( { "type": semantic_group, "ref_id": entity_ann["id"], "resource_name": "UMLS", "cuid": cui, "text": "", } ) kb_example["events"] = brat_example["events"] kb_example["relations"] = brat_example["relations"] kb_example["equivalences"] = brat_example["equivalences"] kb_example["attributes"] = brat_example["attributes"] kb_example["notes"] = brat_example["notes"] return kb_example