# coding=utf-8 # Copyright 2022 The HuggingFace Datasets Authors and Simon Ott, github: nomisto # # 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. """ MedMentions is a new manually annotated resource for the recognition of biomedical concepts. What distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000 abstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over 3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines. Corpus: The MedMentions corpus consists of 4,392 papers (Titles and Abstracts) randomly selected from among papers released on PubMed in 2016, that were in the biomedical field, published in the English language, and had both a Title and an Abstract. Annotators: We recruited a team of professional annotators with rich experience in biomedical content curation to exhaustively annotate all UMLS® (2017AA full version) entity mentions in these papers. Annotation quality: We did not collect stringent IAA (Inter-annotator agreement) data. To gain insight on the annotation quality of MedMentions, we randomly selected eight papers from the annotated corpus, containing a total of 469 concepts. Two biologists ('Reviewer') who did not participate in the annotation task then each reviewed four papers. The agreement between Reviewers and Annotators, an estimate of the Precision of the annotations, was 97.3%. For more information visit: https://github.com/chanzuckerberg/MedMentions """ import itertools as it from typing import List import datasets from .bigbiohub import kb_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _LANGUAGES = ['English'] _PUBMED = True _LOCAL = False _CITATION = """\ @misc{mohan2019medmentions, title={MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts}, author={Sunil Mohan and Donghui Li}, year={2019}, eprint={1902.09476}, archivePrefix={arXiv}, primaryClass={cs.CL} } """ _DATASETNAME = "medmentions" _DISPLAYNAME = "MedMentions" _DESCRIPTION = """\ MedMentions is a new manually annotated resource for the recognition of biomedical concepts. What distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000 abstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over 3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines. Corpus: The MedMentions corpus consists of 4,392 papers (Titles and Abstracts) randomly selected from among papers released on PubMed in 2016, that were in the biomedical field, published in the English language, and had both a Title and an Abstract. Annotators: We recruited a team of professional annotators with rich experience in biomedical content curation to exhaustively annotate all UMLS® (2017AA full version) entity mentions in these papers. Annotation quality: We did not collect stringent IAA (Inter-annotator agreement) data. To gain insight on the annotation quality of MedMentions, we randomly selected eight papers from the annotated corpus, containing a total of 469 concepts. Two biologists ('Reviewer') who did not participate in the annotation task then each reviewed four papers. The agreement between Reviewers and Annotators, an estimate of the Precision of the annotations, was 97.3%. """ _HOMEPAGE = "https://github.com/chanzuckerberg/MedMentions" _LICENSE = 'Creative Commons Zero v1.0 Universal' _URLS = { "medmentions_full": [ "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator.txt.gz", "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator_pmids_trng.txt", "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator_pmids_dev.txt", "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator_pmids_test.txt", ], "medmentions_st21pv": [ "https://github.com/chanzuckerberg/MedMentions/raw/master/st21pv/data/corpus_pubtator.txt.gz", "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator_pmids_trng.txt", "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator_pmids_dev.txt", "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator_pmids_test.txt", ], } _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_DISAMBIGUATION, Tasks.NAMED_ENTITY_RECOGNITION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" class MedMentionsDataset(datasets.GeneratorBasedBuilder): """MedMentions dataset for named-entity disambiguation (NED)""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="medmentions_full_source", version=SOURCE_VERSION, description="MedMentions Full source schema", schema="source", subset_id="medmentions_full", ), BigBioConfig( name="medmentions_full_bigbio_kb", version=BIGBIO_VERSION, description="MedMentions Full BigBio schema", schema="bigbio_kb", subset_id="medmentions_full", ), BigBioConfig( name="medmentions_st21pv_source", version=SOURCE_VERSION, description="MedMentions ST21pv source schema", schema="source", subset_id="medmentions_st21pv", ), BigBioConfig( name="medmentions_st21pv_bigbio_kb", version=BIGBIO_VERSION, description="MedMentions ST21pv BigBio schema", schema="bigbio_kb", subset_id="medmentions_st21pv", ), ] DEFAULT_CONFIG_NAME = "medmentions_full_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "pmid": datasets.Value("string"), "passages": [ { "type": datasets.Value("string"), "text": datasets.Sequence(datasets.Value("string")), "offsets": datasets.Sequence([datasets.Value("int32")]), } ], "entities": [ { "text": datasets.Sequence(datasets.Value("string")), "offsets": datasets.Sequence([datasets.Value("int32")]), "concept_id": datasets.Value("string"), "semantic_type_id": datasets.Sequence( 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]: urls = _URLS[self.config.subset_id] ( corpus_path, pmids_train, pmids_dev, pmids_test, ) = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"corpus_path": corpus_path, "pmids_path": pmids_train}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"corpus_path": corpus_path, "pmids_path": pmids_test}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"corpus_path": corpus_path, "pmids_path": pmids_dev}, ), ] def _generate_examples(self, corpus_path, pmids_path): with open(pmids_path, encoding="utf8") as infile: pmids = infile.readlines() pmids = {int(x.strip()) for x in pmids} if self.config.schema == "source": with open(corpus_path, encoding="utf8") as corpus: for document in self._generate_parsed_documents(corpus, pmids): yield document["pmid"], document elif self.config.schema == "bigbio_kb": uid = it.count(0) with open(corpus_path, encoding="utf8") as corpus: for document in self._generate_parsed_documents(corpus, pmids): document["id"] = next(uid) document["document_id"] = document.pop("pmid") entities_ = [] for entity in document["entities"]: for type in entity["semantic_type_id"]: entities_.append( { "id": next(uid), "type": type, "text": entity["text"], "offsets": entity["offsets"], "normalized": [ { "db_name": "UMLS", "db_id": entity["concept_id"].split(":")[-1], } ], } ) document["entities"] = entities_ for passage in document["passages"]: passage["id"] = next(uid) document["relations"] = [] document["events"] = [] document["coreferences"] = [] yield document["document_id"], document def _generate_parsed_documents(self, fstream, pmids): for raw_document in self._generate_raw_documents(fstream): if self._parse_pmid(raw_document) in pmids: yield self._parse_document(raw_document) def _generate_raw_documents(self, fstream): raw_document = [] for line in fstream: if line.strip(): raw_document.append(line.strip()) elif raw_document: yield raw_document raw_document = [] # needed for last document if raw_document: yield raw_document def _parse_pmid(self, raw_document): pmid, _ = raw_document[0].split("|", 1) return int(pmid) def _parse_document(self, raw_document): pmid, type, title = raw_document[0].split("|", 2) pmid_, type, abstract = raw_document[1].split("|", 2) passages = [ {"type": "title", "text": [title], "offsets": [[0, len(title)]]}, { "type": "abstract", "text": [abstract], "offsets": [[len(title) + 1, len(title) + len(abstract) + 1]], }, ] entities = [] for line in raw_document[2:]: ( pmid_, start_idx, end_idx, mention, semantic_type_id, entity_id, ) = line.split("\t") entity = { "offsets": [[int(start_idx), int(end_idx)]], "text": [mention], "semantic_type_id": semantic_type_id.split(","), "concept_id": entity_id, } entities.append(entity) return {"pmid": int(pmid), "entities": entities, "passages": passages}