# 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 collections import itertools from pathlib import Path from typing import Dict, List, Tuple import datasets from bioc import biocxml from .bigbiohub import kb_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks from .bigbiohub import get_texts_and_offsets_from_bioc_ann _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @article{islamaj2021nlm, title = { NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition }, author = { Islamaj, Rezarta and Wei, Chih-Hsuan and Cissel, David and Miliaras, Nicholas and Printseva, Olga and Rodionov, Oleg and Sekiya, Keiko and Ward, Janice and Lu, Zhiyong }, year = 2021, journal = {Journal of Biomedical Informatics}, publisher = {Elsevier}, volume = 118, pages = 103779 } """ _DATASETNAME = "nlm_gene" _DISPLAYNAME = "NLM-Gene" _DESCRIPTION = """\ NLM-Gene consists of 550 PubMed articles, from 156 journals, and contains more \ than 15 thousand unique gene names, corresponding to more than five thousand \ gene identifiers (NCBI Gene taxonomy). This corpus contains gene annotation data \ from 28 organisms. The annotated articles contain on average 29 gene names, and \ 10 gene identifiers per article. These characteristics demonstrate that this \ article set is an important benchmark dataset to test the accuracy of gene \ recognition algorithms both on multi-species and ambiguous data. The NLM-Gene \ corpus will be invaluable for advancing text-mining techniques for gene \ identification tasks in biomedical text. """ _HOMEPAGE = "https://zenodo.org/record/5089049" _LICENSE = 'Creative Commons Zero v1.0 Universal' _URLS = { "source": "https://zenodo.org/record/5089049/files/NLM-Gene-Corpus.zip", "bigbio_kb": "https://zenodo.org/record/5089049/files/NLM-Gene-Corpus.zip", } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class NLMGeneDataset(datasets.GeneratorBasedBuilder): """NLM-Gene Dataset for gene entities""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="nlm_gene_source", version=SOURCE_VERSION, description="NlM Gene source schema", schema="source", subset_id="nlm_gene", ), BigBioConfig( name="nlm_gene_bigbio_kb", version=BIGBIO_VERSION, description="NlM Gene BigBio schema", schema="bigbio_kb", subset_id="nlm_gene", ), ] DEFAULT_CONFIG_NAME = "nlm_gene_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": if self.config.schema == "source": # this is a variation on the BioC format features = datasets.Features( { "passages": [ { "document_id": datasets.Value("string"), "type": datasets.Value("string"), "text": datasets.Value("string"), "entities": [ { "id": datasets.Value("string"), "offsets": [[datasets.Value("int32")]], "text": [datasets.Value("string")], "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[self.config.schema] data_dir = Path(dl_manager.download_and_extract(urls)) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_dir / "Corpus", "file_name": "Pmidlist.Train.txt", "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_dir / "Corpus", "file_name": "Pmidlist.Test.txt", "split": "test", }, ), ] @staticmethod def _get_bioc_entity( span, db_id_key="NCBI Gene identifier", splitters=",;|-" ) -> dict: """Parse BioC entity annotation.""" offsets, texts = get_texts_and_offsets_from_bioc_ann(span) db_ids = span.infons.get(db_id_key, "-1") # Find connector between db_ids for the normalization, if not found, use default connector = "|" for splitter in list(splitters): if splitter in db_ids: connector = splitter normalized = [ {"db_name": "NCBIGene", "db_id": db_id} for db_id in db_ids.split(connector) ] return { "id": span.id, "offsets": offsets, "text": texts, "type": span.infons["type"], "normalized": normalized, } def _generate_examples( self, filepath: Path, file_name: str, split: str ) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" if self.config.schema == "source": with open(filepath / file_name, encoding="utf-8") as f: contents = f.readlines() for uid, content in enumerate(contents): file_id = content.replace("\n", "") file_path = filepath / "FINAL" / f"{file_id}.BioC.XML" reader = biocxml.BioCXMLDocumentReader(file_path.as_posix()) for xdoc in reader: yield uid, { "passages": [ { "document_id": xdoc.id, "type": passage.infons["type"], "text": passage.text, "entities": [ self._get_bioc_entity(span) for span in passage.annotations ], } for passage in xdoc.passages ] } elif self.config.schema == "bigbio_kb": with open(filepath / file_name, encoding="utf-8") as f: contents = f.readlines() uid = 0 # global unique id for i, content in enumerate(contents): file_id = content.replace("\n", "") file_path = filepath / "FINAL" / f"{file_id}.BioC.XML" reader = biocxml.BioCXMLDocumentReader(file_path.as_posix()) for xdoc in reader: data = { "id": uid, "document_id": xdoc.id, "passages": [], "entities": [], "relations": [], "events": [], "coreferences": [], } uid += 1 char_start = 0 # passages must not overlap and spans must cover the entire document for passage in xdoc.passages: offsets = [[char_start, char_start + len(passage.text)]] char_start = char_start + len(passage.text) + 1 data["passages"].append( { "id": uid, "type": passage.infons["type"], "text": [passage.text], "offsets": offsets, } ) uid += 1 # entities for passage in xdoc.passages: for span in passage.annotations: ent = self._get_bioc_entity( span, db_id_key="NCBI Gene identifier" ) ent["id"] = uid # override BioC default id data["entities"].append(ent) uid += 1 yield i, data