# 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 itertools import os import re 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{Wei2015, author={Wei, Chih-Hsuan and Kao, Hung-Yu and Lu, Zhiyong}, title={GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains}, journal={BioMed Research International}, year={2015}, month={Aug}, day={25}, publisher={Hindawi Publishing Corporation}, volume={2015}, pages={918710}, issn={2314-6133}, doi={10.1155/2015/918710}, url={https://doi.org/10.1155/2015/918710} } """ _DATASETNAME = "gnormplus" _DISPLAYNAME = "GNormPlus" _DESCRIPTION = """\ We re-annotated two existing gene corpora. The BioCreative II GN corpus is a widely used data set for benchmarking GN tools and includes document-level annotations for a total of 543 articles (281 in its training set; and 262 in test). The Citation GIA Test Collection was recently created for gene indexing at the NLM and includes 151 PubMed abstracts with both mention-level and document-level annotations. They are selected because both have a focus on human genes. For both corpora, we added annotations of gene families and protein domains. For the BioCreative GN corpus, we also added mention-level gene annotations. As a result, in our new corpus, there are a total of 694 PubMed articles. PubTator was used as our annotation tool along with BioC formats. """ _HOMEPAGE = "https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/gnormplus/" _LICENSE = "UNKNOWN" _URLS = { _DATASETNAME: "https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/download/GNormPlus/GNormPlusCorpus.zip" } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class GnormplusDataset(datasets.GeneratorBasedBuilder): """Dataset loader for GNormPlus corpus.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="gnormplus_source", version=SOURCE_VERSION, description="gnormplus source schema", schema="source", subset_id="gnormplus", ), BigBioConfig( name="gnormplus_bigbio_kb", version=BIGBIO_VERSION, description="gnormplus BigBio schema", schema="bigbio_kb", subset_id="gnormplus", ), ] DEFAULT_CONFIG_NAME = "gnormplus_source" _re_tax_id = re.compile(r"(?P\d+)\([tT]ax:(?P\d+)\)") def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "doc_id": datasets.Value("string"), "passages": [ { "text": datasets.Value("string"), "type": datasets.Value("string"), "location": { "offset": datasets.Value("int64"), "length": datasets.Value("int64"), }, } ], "entities": [ { "id": datasets.Value("string"), "type": datasets.Value("string"), "text": datasets.Sequence(datasets.Value("string")), "offsets": datasets.Sequence([datasets.Value("int32")]), "normalized": [ { "db_name": datasets.Value("string"), "db_id": datasets.Value("string"), "tax_id": datasets.Value("string"), } ], } ], } ) elif self.config.schema == "bigbio_kb": features = kb_features else: raise NotImplementedError(self.config.schema) 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) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # Whatever you put in gen_kwargs will be passed to _generate_examples gen_kwargs={ "filepaths": [ os.path.join(data_dir, "GNormPlusCorpus/BC2GNtrain.BioC.xml"), # This sub-part of the corpus is part of the GIA Test Collection, however in # the paper they used it only for training their models. So we also add it to the # training split. os.path.join(data_dir, "GNormPlusCorpus/NLMIAT.BioC.xml"), ], }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepaths": [ os.path.join(data_dir, "GNormPlusCorpus/BC2GNtest.BioC.xml"), ] }, ), ] def _parse_bioc_entity(self, uid, bioc_ann, db_id_key="NCBIGene", insert_tax_id=False): offsets, texts = get_texts_and_offsets_from_bioc_ann(bioc_ann) _type = bioc_ann.infons["type"] # parse db ids normalized = [] if _type in bioc_ann.infons: for _id in bioc_ann.infons[_type].split(","): match = self._re_tax_id.match(_id) if match: _id = match.group("db_id") n = {"db_name": db_id_key, "db_id": _id} if insert_tax_id: n["tax_id"] = match.group("tax_id") if match else None normalized.append(n) return { "id": uid, "offsets": offsets, "text": texts, "type": _type, "normalized": normalized, } def _generate_examples(self, filepaths) -> Tuple[int, Dict]: uid = map(str, itertools.count(start=0, step=1)) for filepath in filepaths: with open(filepath, "r") as fp: collection = biocxml.load(fp) for _, document in enumerate(collection.documents): idx = next(uid) text = " ".join([passage.text for passage in document.passages]) insert_tax = self.config.schema == "source" entities = [ self._parse_bioc_entity(next(uid), entity, insert_tax_id=insert_tax) for passage in document.passages for entity in passage.annotations ] # Some of the entities have a off-by-one error. Correct these annotations! self.adjust_entity_offsets(text, entities) if self.config.schema == "source": features = { "doc_id": document.id, "passages": [ { "text": passage.text, "type": passage.infons["type"], "location": { "offset": passage.offset, "length": passage.total_span.length, }, } for passage in document.passages ], "entities": entities, } yield idx, features elif self.config.schema == "bigbio_kb": # passage offsets/lengths do not connect, recalculate them for this schema. passage_spans = [] start = 0 for passage in document.passages: end = start + len(passage.text) passage_spans.append((start, end)) start = end + 1 features = { "id": next(uid), "document_id": document.id, "passages": [ { "id": next(uid), "type": passage.infons["type"], "text": [passage.text], "offsets": [span], } for passage, span in zip(document.passages, passage_spans) ], "entities": entities, "events": [], "coreferences": [], "relations": [], } yield idx, features else: raise NotImplementedError(self.config.schema) def adjust_entity_offsets(self, text: str, entities: List[Dict]): for entity in entities: start, end = entity["offsets"][0] entity_mention = entity["text"][0] if not text[start:end] == entity_mention: if text[start - 1 : end - 1] == entity_mention: entity["offsets"] = [(start - 1, end - 1)] elif text[start : end - 1] == entity_mention: entity["offsets"] = [(start, end - 1)]