# 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. """ The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. """ import os from typing import Dict, Iterator, List, Tuple import datasets from bioc import pubtator from .bigbiohub import kb_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @article{Dogan2014NCBIDC, title = {NCBI disease corpus: A resource for disease name recognition and concept normalization}, author = {Rezarta Islamaj Dogan and Robert Leaman and Zhiyong Lu}, year = 2014, journal = {Journal of biomedical informatics}, volume = 47, pages = {1--10} } """ _DATASETNAME = "ncbi_disease" _DISPLAYNAME = "NCBI Disease" _DESCRIPTION = """\ The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. """ _HOMEPAGE = "https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/" _LICENSE = 'Creative Commons Zero v1.0 Universal' _URLS = { _DATASETNAME: { datasets.Split.TRAIN: "https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBItrainset_corpus.zip", datasets.Split.TEST: "https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBItestset_corpus.zip", datasets.Split.VALIDATION: "https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBIdevelopset_corpus.zip", } } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class NCBIDiseaseDataset(datasets.GeneratorBasedBuilder): """NCBI Disease""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="ncbi_disease_source", version=SOURCE_VERSION, description="NCBI Disease source schema", schema="source", subset_id="ncbi_disease", ), BigBioConfig( name="ncbi_disease_bigbio_kb", version=BIGBIO_VERSION, description="NCBI Disease BigBio schema", schema="bigbio_kb", subset_id="ncbi_disease", ), ] DEFAULT_CONFIG_NAME = "ncbi_disease_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "pmid": datasets.Value("string"), "title": datasets.Value("string"), "abstract": datasets.Value("string"), "mentions": [ { "concept_id": datasets.Value("string"), "type": datasets.Value("string"), "text": datasets.Value("string"), "offsets": datasets.Sequence(datasets.Value("int32")), } ], } ) 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]: urls = _URLS[_DATASETNAME] data_dir = dl_manager.download_and_extract(urls) train_filename = "NCBItrainset_corpus.txt" test_filename = "NCBItestset_corpus.txt" dev_filename = "NCBIdevelopset_corpus.txt" train_filepath = os.path.join(data_dir[datasets.Split.TRAIN], train_filename) test_filepath = os.path.join(data_dir[datasets.Split.TEST], test_filename) dev_filepath = os.path.join(data_dir[datasets.Split.VALIDATION], dev_filename) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": train_filepath, "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": test_filepath, "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": dev_filepath, "split": "dev", }, ), ] def _generate_examples( self, filepath: str, split: str ) -> Iterator[Tuple[str, Dict]]: if self.config.schema == "source": for i, source_example in enumerate(self._pubtator_to_source(filepath)): # Some examples are duplicated in NCBI Disease. We have to make them unique to # avoid and error from datasets. yield str(i) + "_" + source_example["pmid"], source_example elif self.config.schema == "bigbio_kb": seen = [] for kb_example in self._pubtator_to_bigbio_kb(filepath): # Some examples are duplicated in NCBI Disease. Avoid yielding more than once. if kb_example["id"] in seen: continue yield kb_example["id"], kb_example seen.append(kb_example["id"]) @staticmethod def _pubtator_to_source(filepath: Dict) -> Iterator[Dict]: with open(filepath, "r") as f: for doc in pubtator.iterparse(f): source_example = { "pmid": doc.pmid, "title": doc.title, "abstract": doc.abstract, "mentions": [ { "concept_id": mention.id, "type": mention.type, "text": mention.text, "offsets": [mention.start, mention.end], } for mention in doc.annotations ], } yield source_example @staticmethod def _pubtator_to_bigbio_kb(filepath: Dict) -> Iterator[Dict]: with open(filepath, "r") as f: unified_example = {} for doc in pubtator.iterparse(f): unified_example["id"] = doc.pmid unified_example["document_id"] = doc.pmid unified_example["passages"] = [ { "id": doc.pmid + "_title", "type": "title", "text": [doc.title], "offsets": [[0, len(doc.title)]], }, { "id": doc.pmid + "_abstract", "type": "abstract", "text": [doc.abstract], "offsets": [ [ # +1 assumes the title and abstract will be joined by a space. len(doc.title) + 1, len(doc.title) + 1 + len(doc.abstract), ] ], }, ] unified_entities = [] for i, entity in enumerate(doc.annotations): # We need a unique identifier for this entity, so build it from the document id and entity id unified_entity_id = "_".join([doc.pmid, entity.id, str(i)]) # The user can provide a callable that returns the database name. db_name = "OMIM" if "OMIM" in entity.id else "MESH" normalized = [] for x in entity.id.split("|"): if x.startswith("OMIM") or x.startswith("omim"): normalized.append( {"db_name": "OMIM", "db_id": x.strip().split(":")[-1]} ) else: normalized.append({"db_name": "MESH", "db_id": x.strip()}) unified_entities.append( { "id": unified_entity_id, "type": entity.type, "text": [entity.text], "offsets": [[entity.start, entity.end]], "normalized": normalized, } ) unified_example["entities"] = unified_entities unified_example["relations"] = [] unified_example["events"] = [] unified_example["coreferences"] = [] yield unified_example