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