# 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. """ To this end, we set up a challenge task through BioCreative V to automatically extract CDRs from the literature. More specifically, we designed two challenge tasks: disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. To assist system development and assessment, we created a large annotated text corpus that consists of human annotations of all chemicals, diseases and their interactions in 1,500 PubMed articles. -- 'Overview of the BioCreative V Chemical Disease Relation (CDR) Task' """ import collections import itertools import os 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{DBLP:journals/biodb/LiSJSWLDMWL16, author = {Jiao Li and Yueping Sun and Robin J. Johnson and Daniela Sciaky and Chih{-}Hsuan Wei and Robert Leaman and Allan Peter Davis and Carolyn J. Mattingly and Thomas C. Wiegers and Zhiyong Lu}, title = {BioCreative {V} {CDR} task corpus: a resource for chemical disease relation extraction}, journal = {Database J. Biol. Databases Curation}, volume = {2016}, year = {2016}, url = {https://doi.org/10.1093/database/baw068}, doi = {10.1093/database/baw068}, timestamp = {Thu, 13 Aug 2020 12:41:41 +0200}, biburl = {https://dblp.org/rec/journals/biodb/LiSJSWLDMWL16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _DATASETNAME = "bc5cdr" _DISPLAYNAME = "BC5CDR" _DESCRIPTION = """\ The BioCreative V Chemical Disease Relation (CDR) dataset is a large annotated \ text corpus of human annotations of all chemicals, diseases and their \ interactions in 1,500 PubMed articles. """ _HOMEPAGE = "http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/" _LICENSE = 'Public Domain Mark 1.0' _URLs = { "source": "https://huggingface.co/datasets/bigbio/bc5cdr/resolve/main/CDR_Data.zip", "bigbio_kb": "https://huggingface.co/datasets/bigbio/bc5cdr/resolve/main/CDR_Data.zip", } _SUPPORTED_TASKS = [ Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION, Tasks.RELATION_EXTRACTION, ] _SOURCE_VERSION = "01.05.16" _BIGBIO_VERSION = "1.0.0" class Bc5cdrDataset(datasets.GeneratorBasedBuilder): """ BioCreative V Chemical Disease Relation (CDR) Task. """ DEFAULT_CONFIG_NAME = "bc5cdr_source" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="bc5cdr_source", version=SOURCE_VERSION, description="BC5CDR source schema", schema="source", subset_id="bc5cdr", ), BigBioConfig( name="bc5cdr_bigbio_kb", version=BIGBIO_VERSION, description="BC5CDR simplified BigBio schema", schema="bigbio_kb", subset_id="bc5cdr", ), ] def _info(self): 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"), } ], } ], "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, supervised_keys=None, homepage=_HOMEPAGE, license=str(_LICENSE), citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" my_urls = _URLs[self.config.schema] data_dir = dl_manager.download_and_extract(my_urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join( data_dir, "CDR_Data/CDR.Corpus.v010516/CDR_TrainingSet.BioC.xml" ), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join( data_dir, "CDR_Data/CDR.Corpus.v010516/CDR_TestSet.BioC.xml" ), "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join( data_dir, "CDR_Data/CDR.Corpus.v010516/CDR_DevelopmentSet.BioC.xml", ), "split": "dev", }, ), ] def _get_bioc_entity(self, span, doc_text, db_id_key="MESH"): """Parse BioC entity annotation. Parameters ---------- span : BioCAnnotation BioC entity annotation doc_text : string document text, required to construct text spans db_id_key : str, optional database name used for normalization, by default "MESH" Returns ------- dict entity information """ # offsets = [(loc.offset, loc.offset + loc.length) for loc in span.locations] # texts = [doc_text[i:j] for i, j in offsets] offsets, texts = get_texts_and_offsets_from_bioc_ann(span) db_ids = span.infons[db_id_key] if db_id_key else "-1" # some entities are not linked and # some entities are linked to multiple normalized ids if db_ids == "-1": db_ids_list = [] else: db_ids_list = db_ids.split("|") normalized = [{"db_name": db_id_key, "db_id": db_id} for db_id in db_ids_list] return { "id": span.id, "offsets": offsets, "text": texts, "type": span.infons["type"], "normalized": normalized, } def _get_relations(self, relations, entities): """ BC5CDR provides abstract-level annotations for entity-linked relation pairs rather than materializing links between all surface form mentions of relations. An example from train id=2670794, the relation - (chemical, disease) (D014148, D004211) is materialized as 6 mentions of entity pairs - 2x ('tranexamic acid', 'intravascular coagulation') - 4x ('AMCA', 'intravascular coagulation') """ # index entities by normalized id index = collections.defaultdict(list) for ent in entities: for norm in ent["normalized"]: index[norm["db_id"]].append(ent) index = dict(index) # transform doc-level relations to mention-level rela_mentions = [] for rela in relations: arg1 = rela.infons["Chemical"] arg2 = rela.infons["Disease"] # all mention pairs all_pairs = itertools.product(index[arg1], index[arg2]) for a, b in all_pairs: # create relations linked by entity ids rela_mentions.append( { "id": None, "type": rela.infons["relation"], "arg1_id": a["id"], "arg2_id": b["id"], "normalized": [], } ) return rela_mentions def _get_document_text(self, xdoc): """Build document text for unit testing entity span offsets.""" text = "" for passage in xdoc.passages: pad = passage.offset - len(text) text += (" " * pad) + passage.text return text def _generate_examples( self, filepath, split, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` ): """Yields examples as (key, example) tuples.""" if self.config.schema == "source": reader = biocxml.BioCXMLDocumentReader(str(filepath)) for uid, xdoc in enumerate(reader): doc_text = self._get_document_text(xdoc) yield uid, { "passages": [ { "document_id": xdoc.id, "type": passage.infons["type"], "text": passage.text, "entities": [ self._get_bioc_entity(span, doc_text) for span in passage.annotations ], "relations": [ { "id": rel.id, "type": rel.infons["relation"], "arg1_id": rel.infons["Chemical"], "arg2_id": rel.infons["Disease"], } for rel in xdoc.relations ], } for passage in xdoc.passages ] } elif self.config.schema == "bigbio_kb": reader = biocxml.BioCXMLDocumentReader(str(filepath)) uid = 0 # global unique id for i, xdoc in enumerate(reader): data = { "id": uid, "document_id": xdoc.id, "passages": [], "entities": [], "relations": [], "events": [], "coreferences": [], } uid += 1 doc_text = self._get_document_text(xdoc) 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, doc_text, db_id_key="MESH") ent["id"] = uid # override BioC default id data["entities"].append(ent) uid += 1 # relations relations = self._get_relations(xdoc.relations, data["entities"]) for rela in relations: rela["id"] = uid # assign unique id data["relations"].append(rela) uid += 1 yield i, data