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""" |
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BioScope |
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--- |
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The corpus consists of three parts, namely medical free texts, biological full |
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papers and biological scientific abstracts. The dataset contains annotations at |
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the token level for negative and speculative keywords and at the sentence level |
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for their linguistic scope. The annotation process was carried out by two |
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independent linguist annotators and a chief linguist - also responsible for |
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setting up the annotation guidelines - who resolved cases where the annotators |
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disagreed. The resulting corpus consists of more than 20.000 sentences that were |
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considered for annotation and over 10% of them actually contain one (or more) |
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linguistic annotation suggesting negation or uncertainty. |
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""" |
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|
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import os |
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import re |
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import xml.etree.ElementTree as ET |
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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 = False |
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_CITATION = """\ |
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@article{vincze2008bioscope, |
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title={The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes}, |
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author={Vincze, Veronika and Szarvas, Gy{\"o}rgy and Farkas, Rich{\'a}rd and M{\'o}ra, Gy{\"o}rgy and Csirik, J{\'a}nos}, |
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journal={BMC bioinformatics}, |
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volume={9}, |
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number={11}, |
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pages={1--9}, |
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year={2008}, |
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publisher={BioMed Central} |
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} |
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""" |
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|
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_DATASETNAME = "bioscope" |
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_DISPLAYNAME = "BioScope" |
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|
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|
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_DESCRIPTION = """\ |
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The BioScope corpus consists of medical and biological texts annotated for |
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negation, speculation and their linguistic scope. This was done to allow a |
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comparison between the development of systems for negation/hedge detection and |
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scope resolution. The BioScope corpus was annotated by two independent linguists |
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following the guidelines written by our linguist expert before the annotation of |
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the corpus was initiated. |
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""" |
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|
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_HOMEPAGE = "https://rgai.inf.u-szeged.hu/node/105" |
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|
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_LICENSE = 'Creative Commons Attribution 2.0 Generic' |
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|
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_URLS = { |
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_DATASETNAME: "https://rgai.sed.hu/sites/rgai.sed.hu/files/bioscope.zip", |
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} |
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|
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_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION] |
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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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|
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class BioscopeDataset(datasets.GeneratorBasedBuilder): |
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"""The BioScope corpus consists of medical and biological texts annotated for negation, speculation and their linguistic scope.""" |
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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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BigBioConfig( |
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name="bioscope_source", |
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version=SOURCE_VERSION, |
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description="bioscope source schema", |
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schema="source", |
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subset_id="bioscope", |
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), |
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BigBioConfig( |
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name="bioscope_abstracts_source", |
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version=SOURCE_VERSION, |
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description="bioscope source schema", |
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schema="source", |
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subset_id="bioscope_abstracts", |
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), |
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BigBioConfig( |
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name="bioscope_papers_source", |
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version=SOURCE_VERSION, |
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description="bioscope source schema", |
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schema="source", |
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subset_id="bioscope_papers", |
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), |
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BigBioConfig( |
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name="bioscope_medical_texts_source", |
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version=SOURCE_VERSION, |
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description="bioscope source schema", |
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schema="source", |
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subset_id="bioscope_medical_texts", |
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), |
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BigBioConfig( |
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name="bioscope_bigbio_kb", |
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version=BIGBIO_VERSION, |
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description="bioscope BigBio schema", |
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schema="bigbio_kb", |
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subset_id="bioscope", |
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), |
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BigBioConfig( |
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name="bioscope_abstracts_bigbio_kb", |
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version=BIGBIO_VERSION, |
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description="bioscope BigBio schema", |
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schema="bigbio_kb", |
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subset_id="bioscope_abstracts", |
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), |
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BigBioConfig( |
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name="bioscope_papers_bigbio_kb", |
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version=BIGBIO_VERSION, |
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description="bioscope BigBio schema", |
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schema="bigbio_kb", |
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subset_id="bioscope_papers", |
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), |
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BigBioConfig( |
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name="bioscope_medical_texts_bigbio_kb", |
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version=BIGBIO_VERSION, |
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description="bioscope BigBio schema", |
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schema="bigbio_kb", |
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subset_id="bioscope_medical_texts", |
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), |
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] |
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|
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DEFAULT_CONFIG_NAME = "bioscope_source" |
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|
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def _info(self) -> datasets.DatasetInfo: |
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|
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"document_id": datasets.Value("string"), |
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"document_type": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"entities": [ |
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{ |
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"offsets": datasets.Sequence([datasets.Value("int32")]), |
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"text": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"id": datasets.Value("string"), |
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"normalized": [ |
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{ |
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"db_name": datasets.Value("string"), |
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"db_id": datasets.Value("string"), |
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} |
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], |
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} |
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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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urls = _URLS[_DATASETNAME] |
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data_dir = dl_manager.download_and_extract(urls) |
|
|
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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_files": 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_files: Path) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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sentences = self._load_sentences(data_files) |
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if self.config.schema == "source": |
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for guid, sentence_tuple in enumerate(sentences): |
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document_type, sentence = sentence_tuple |
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example = self._create_example(sentence_tuple) |
|
example["document_type"] = f"{document_type}_{sentence.attrib['id']}" |
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example["text"] = "".join(sentence_tuple[1].itertext()) |
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yield guid, example |
|
|
|
elif self.config.schema == "bigbio_kb": |
|
for guid, sentence_tuple in enumerate(sentences): |
|
document_type, sentence = sentence_tuple |
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example = self._create_example(sentence_tuple) |
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example["id"] = guid |
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example["passages"] = [ |
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{ |
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"id": f"{document_type}_{sentence.attrib['id']}", |
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"type": document_type, |
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"text": ["".join(sentence.itertext())], |
|
"offsets": [(0, len("".join(sentence.itertext())))], |
|
} |
|
] |
|
example["events"] = [] |
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example["coreferences"] = [] |
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example["relations"] = [] |
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yield guid, example |
|
|
|
def _load_sentences(self, data_files: Path) -> List: |
|
""" |
|
Returns a list of tuples (Document type, iterator from dataset) |
|
""" |
|
if self.config.subset_id.__contains__("abstracts"): |
|
sentences = self._concat_iterators( |
|
( |
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"Abstract", |
|
ET.parse(os.path.join(data_files, "abstracts.xml")) |
|
.getroot() |
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.iter("sentence"), |
|
) |
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) |
|
elif self.config.subset_id.__contains__("papers"): |
|
sentences = self._concat_iterators( |
|
( |
|
"Paper", |
|
ET.parse(os.path.join(data_files, "full_papers.xml")) |
|
.getroot() |
|
.iter("sentence"), |
|
) |
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) |
|
elif self.config.subset_id.__contains__("medical_texts"): |
|
sentences = self._concat_iterators( |
|
( |
|
"Medical text", |
|
ET.parse( |
|
os.path.join( |
|
data_files, "clinical_merger/clinical_records_anon.xml" |
|
) |
|
) |
|
.getroot() |
|
.iter("sentence"), |
|
) |
|
) |
|
else: |
|
abstracts = ( |
|
ET.parse(os.path.join(data_files, "abstracts.xml")) |
|
.getroot() |
|
.iter("sentence") |
|
) |
|
papers = ( |
|
ET.parse(os.path.join(data_files, "full_papers.xml")) |
|
.getroot() |
|
.iter("sentence") |
|
) |
|
medical_texts = ( |
|
ET.parse( |
|
os.path.join( |
|
data_files, "clinical_merger/clinical_records_anon.xml" |
|
) |
|
) |
|
.getroot() |
|
.iter("sentence") |
|
) |
|
sentences = self._concat_iterators( |
|
("Abstract", abstracts), |
|
("Paper", papers), |
|
("Medical text", medical_texts), |
|
) |
|
return sentences |
|
|
|
@staticmethod |
|
def _concat_iterators(*iterator_tuple): |
|
for document_type, iterator in iterator_tuple: |
|
for element in iterator: |
|
yield document_type, element |
|
|
|
def _create_example(self, sentence_tuple): |
|
document_type, sentence = sentence_tuple |
|
document_type_prefix = document_type[0] |
|
|
|
example = {} |
|
example["document_id"] = f"{document_type_prefix}_{sentence.attrib['id']}" |
|
example["entities"] = self._extract_entities(sentence, document_type_prefix) |
|
return example |
|
|
|
def _extract_entities(self, sentence, document_type_prefix): |
|
text = "".join(sentence.itertext()) |
|
entities = [] |
|
xcopes = dict([(xcope.attrib["id"], xcope) for xcope in sentence.iter("xcope")]) |
|
cues = dict([(cue.attrib["ref"], cue) for cue in sentence.iter("cue")]) |
|
for idx, xcope in xcopes.items(): |
|
|
|
if cues.get(idx) is None: |
|
continue |
|
entities.append( |
|
{ |
|
"id": f"{document_type_prefix}_{idx}", |
|
"type": cues.get(idx).attrib["type"], |
|
"text": ["".join(xcope.itertext())], |
|
"offsets": self._extract_offsets( |
|
text=text, entity_text="".join(xcope.itertext()) |
|
), |
|
"normalized": [], |
|
} |
|
) |
|
return entities |
|
|
|
def _extract_offsets(self, text, entity_text): |
|
return [(text.find(entity_text), text.find(entity_text) + len(entity_text))] |
|
|
