# 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. """ Evaluation of Word Sense Disambiguation methods (WSD) in the biomedical domain is difficult because the available resources are either too small or too focused on specific types of entities (e.g. diseases or genes). We have developed a method that can be used to automatically develop a WSD test collection using the Unified Medical Language System (UMLS) Metathesaurus and the manual MeSH indexing of MEDLINE. The resulting dataset is called MSH WSD and consists of 106 ambiguous abbreviations, 88 ambiguous terms and 9 which are a combination of both, for a total of 203 ambiguous words. Each instance containing the ambiguous word was assigned a CUI from the 2009AB version of the UMLS. For each ambiguous term/abbreviation, the data set contains a maximum of 100 instances per sense obtained from MEDLINE; totaling 37,888 ambiguity cases in 37,090 MEDLINE citations. Note from the Author how to load dataset: 1) Download the file MSHCorpus.zip (Link "MSHWSD Data Set") from https://lhncbc.nlm.nih.gov/ii/areas/WSD/collaboration.html 2) Set kwarg data_dir to the directory containing MSHCorpus.zip """ import itertools as it import os import re from dataclasses import dataclass from pathlib import Path from typing import Dict, List, Tuple import datasets from .bigbiohub import kb_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _LANGUAGES = ['English'] _PUBMED = True _LOCAL = True _CITATION = """\ @article{jimeno2011exploiting, title={Exploiting MeSH indexing in MEDLINE to generate a data set for word sense disambiguation}, author={Jimeno-Yepes, Antonio J and McInnes, Bridget T and Aronson, Alan R}, journal={BMC bioinformatics}, volume={12}, number={1}, pages={1--14}, year={2011}, publisher={BioMed Central} } """ _DESCRIPTION = """\ Evaluation of Word Sense Disambiguation methods (WSD) in the biomedical domain is difficult because the available resources are either too small or too focused on specific types of entities (e.g. diseases or genes). We have developed a method that can be used to automatically develop a WSD test collection using the Unified Medical Language System (UMLS) Metathesaurus and the manual MeSH indexing of MEDLINE. The resulting dataset is called MSH WSD and consists of 106 ambiguous abbreviations, 88 ambiguous terms and 9 which are a combination of both, for a total of 203 ambiguous words. Each instance containing the ambiguous word was assigned a CUI from the 2009AB version of the UMLS. For each ambiguous term/abbreviation, the data set contains a maximum of 100 instances per sense obtained from MEDLINE; totaling 37,888 ambiguity cases in 37,090 MEDLINE citations. """ _DATASETNAME = "msh_wsd" _DISPLAYNAME = "MSH WSD" _HOMEPAGE = "https://lhncbc.nlm.nih.gov/ii/areas/WSD/collaboration.html" _LICENSE = 'UMLS - Metathesaurus License Agreement' _URLS = {_DATASETNAME: ""} _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_DISAMBIGUATION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" @dataclass class MshWsdBigBioConfig(BigBioConfig): schema: str = "source" name: str = "msh_wsd_source" version: datasets.Version = datasets.Version(_SOURCE_VERSION) description: str = "MSH-WSD source schema" subset_id: str = "msh_wsd" class MshWsdDataset(datasets.GeneratorBasedBuilder): """Biomedical Word Sense Disambiguation (WSD).""" uid = it.count(0) SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ MshWsdBigBioConfig( name="msh_wsd_source", version=SOURCE_VERSION, description="MSH-WSD source schema", schema="source", subset_id="msh_wsd", ), MshWsdBigBioConfig( name="msh_wsd_bigbio_kb", version=BIGBIO_VERSION, description="MSH-WSD BigBio schema", schema="bigbio_kb", subset_id="msh_wsd", ), ] BUILDER_CONFIG_CLASS = MshWsdBigBioConfig def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "ambiguous_word": datasets.Value("string"), "sentences": [ { "pmid": datasets.Value("string"), "text": datasets.Value("string"), "label": datasets.Value("string"), } ], "choices": [ { "label": datasets.Value("string"), "concept": 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.""" if self.config.data_dir is None: raise ValueError( "This is a local dataset. Please pass the data_dir kwarg to load_dataset." ) else: data_dir = dl_manager.download_and_extract( os.path.join(self.config.data_dir, "MSHCorpus.zip") ) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "data_dir": Path(data_dir), }, ), ] def _generate_examples(self, data_dir: Path) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" data_dir = data_dir / "MSHCorpus" concepts = data_dir / "benchmark_mesh.txt" with concepts.open() as f: concepts = f.readlines() concepts = [x.strip().split("\t") for x in concepts] concept_map = { cuis[0]: {f"M{num}": cui for num, cui in enumerate(cuis[1:], 1)} for cuis in concepts } files = list(data_dir.glob("*arff")) for guid, file in enumerate(files): if self.config.schema == "source": for example in self._parse_document(concept_map, file): yield guid, example elif self.config.schema == "bigbio_kb": for document in self._parse_document(concept_map, file): for example in self._source_to_kb(document): yield example["id"], example def _parse_document(self, concept_map, file: Path): with file.open(mode="r", encoding="iso-8859-1") as f: content = f.readlines() content = [x.strip() for x in content] # search line number of @DATA, sometimes 6 or 7 start_l = None for number, line in enumerate(content): if line.startswith("@DATA"): start_l = number + 1 break assert start_l is not None amb_word = file.with_suffix("").name[: -len("_pmids_tagged")] sentences = [] for line in content[start_l:]: # cant use , or ," ", as seperator m_pmid = re.search("[0-9]+(?=(,))", line) pmid = m_pmid.group() m_label = re.search("(?<=(,))M[0-9]+", line) label = m_label.group() citation = line[m_pmid.span()[1] + 1 : m_label.span()[0] - 1].strip('"') sentences.append({"pmid": pmid, "text": citation, "label": label}) yield { "ambiguous_word": amb_word, "sentences": sentences, "choices": [ {"label": key, "concept": value} for key, value in concept_map[amb_word].items() ], } def _source_to_kb(self, document): choices = {x["label"]: x["concept"] for x in document["choices"]} for sentence in document["sentences"]: document_ = {} document_["events"] = [] document_["relations"] = [] document_["coreferences"] = [] document_["id"] = next(self.uid) document_["document_id"] = sentence["pmid"] document_["passages"] = [ { "id": next(self.uid), "type": "", "text": [sentence["text"]], "offsets": [[0, len(sentence["text"])]], } ] document_["entities"] = [ { "id": next(self.uid), "type": "ambiguous_word", "text": [document["ambiguous_word"]], "offsets": [self._parse_offset(sentence["text"])], "normalized": [ {"db_name": "MeSH", "db_id": choices[sentence["label"]]} ], } ] yield document_ def _parse_offset(self, sentence): m = re.search("(?<=()).+(?=())", sentence) return m.span()