# 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. from pathlib import Path from typing import Dict, List, Tuple, Union import datasets import jsonlines from .bigbiohub import kb_features, BigBioConfig, Tasks _CITATION = """\ @InProceedings{wuehrl_klinger_2022, author = {Wuehrl, Amelie and Klinger, Roman}, title = {Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)}, booktitle = {Proceedings of The 13th Language Resources and Evaluation Conference}, month = {June}, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association} } """ _DATASETNAME = "bear" _DISPLAYNAME = "BEAR" _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _LICENSE = "CC_BY_SA_4p0" _DESCRIPTION = """\ A dataset of 2100 Twitter posts annotated with 14 different types of biomedical entities (e.g., disease, treatment, risk factor, etc.) and 20 relation types (including caused, treated, worsens, etc.). """ _HOMEPAGE = "https://www.ims.uni-stuttgart.de/en/research/resources/corpora/bioclaim/" _URLS = { _DATASETNAME: "https://www.ims.uni-stuttgart.de/documents/ressourcen/korpora/bioclaim/bear-corpus-WuehrlKlinger-\ LREC2022.zip", } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class BearDataset(datasets.GeneratorBasedBuilder): """ BEAR: A Corpus of Biomedical Entities and Relations A dataset of 2100 Twitter posts annotated with 14 different types of biomedical entities (e.g., disease, treatment, risk factor, etc.) and 20 relation types (including caused, treated, worsens, etc.). """ SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="bear_source", version=SOURCE_VERSION, description="bear source schema", schema="source", subset_id="bear", ), BigBioConfig( name="bear_bigbio_kb", version=BIGBIO_VERSION, description="bear BigBio schema", schema="bigbio_kb", subset_id="bear", ), ] DEFAULT_CONFIG_NAME = "bear_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "document_id": datasets.Value("string"), "document_text": datasets.Value("string"), "entities": [ { "id": datasets.Value("string"), "type": datasets.Value("string"), "text": datasets.Value("string"), "offsets": datasets.Sequence(datasets.Value("int32")), } ], "relations": [ { "id": datasets.Value("string"), "type": datasets.Value("string"), "arg1_id": datasets.Value("string"), "arg2_id": datasets.Value("string"), } ], } ) elif self.config.schema == "bigbio_kb": features = kb_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_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) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": Path(data_dir) / "corpus" / "bear.jsonl", }, ), ] def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" uid = 0 input_file = filepath with jsonlines.open(input_file, "r") as file: for document in file: document_id: str = document.pop("doc_id") document_text: str = document.pop("doc_text") entities: Dict[str, Dict[str, Union[str, int]]] = document.pop("entities", {}) relations: List[Dict[str, Union[str, int]]] = document.pop("relations", []) if not entities and not relations: continue if self.config.schema == "source": source_example = self._to_source_example( document_id=document_id, document_text=document_text, entities=entities, relations=relations, ) yield uid, source_example elif self.config.schema == "bigbio_kb": bigbio_example = self._to_bigbio_example( document_id=document_id, document_text=document_text, entities=entities, relations=relations, ) yield uid, bigbio_example uid += 1 def _to_source_example( self, document_id: str, document_text: str, entities: Dict[str, Dict[str, Union[str, int]]], relations: List[Dict[str, Union[str, int]]], ) -> Dict: source_example = { "document_id": document_id, "document_text": document_text, } # Capture Entities _entities = [] for id, entity_values in entities.items(): if not entity_values: continue start = entity_values.pop("begin") end = entity_values.pop("end") type = entity_values.pop("tag") text = document_text[start:end] entity = { "id": f"{document_id}_{id}", "type": type, "text": text, "offsets": [start, end], } _entities.append(entity) source_example["entities"] = _entities # Capture Relations _relations = [] for id, relation_values in enumerate(relations): end_entity = relation_values.pop("end_entity") rel_tag = relation_values.pop("rel_tag") start_entity = relation_values.pop("start_entity") relation = { "id": f"{document_id}_relation_{id}", "type": rel_tag, "arg1_id": f"{document_id}_{start_entity}", "arg2_id": f"{document_id}_{end_entity}", } _relations.append(relation) source_example["relations"] = _relations return source_example def _to_bigbio_example( self, document_id: str, document_text: str, entities: Dict[str, Dict[str, Union[str, int]]], relations: List[Dict[str, Union[str, int]]], ) -> Dict: bigbio_example = { "id": f"{document_id}_id", "document_id": document_id, "passages": [ { "id": f"{document_id}_passage", "type": "social_media_text", "text": [document_text], "offsets": [[0, len(document_text)]], } ], "events": [], "coreferences": [], } # Capture Entities _entities = [] for id, entity_values in entities.items(): if not entity_values: continue start = entity_values.pop("begin") end = entity_values.pop("end") type = entity_values.pop("tag") text = document_text[start:end] entity = { "id": f"{document_id}_{id}", "type": type, "text": [text], "offsets": [[start, end]], "normalized": [], } _entities.append(entity) bigbio_example["entities"] = _entities # Capture Relations _relations = [] for id, relation_values in enumerate(relations): end_entity = relation_values.pop("end_entity") rel_tag = relation_values.pop("rel_tag") start_entity = relation_values.pop("start_entity") relation = { "id": f"{document_id}_relation_{id}", "type": rel_tag, "arg1_id": f"{document_id}_{start_entity}", "arg2_id": f"{document_id}_{end_entity}", "normalized": [], } _relations.append(relation) bigbio_example["relations"] = _relations return bigbio_example