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import xml.etree.ElementTree as ET |
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from typing import Dict, Iterator, List, Tuple |
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import datasets |
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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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_LANGUAGES = ['English'] |
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_PUBMED = True |
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_LOCAL = False |
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_CITATION = """\ |
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@Article{Bagewadi2014, |
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author={Bagewadi, Shweta |
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and Bobi{\'{c}}, Tamara |
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and Hofmann-Apitius, Martin |
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and Fluck, Juliane |
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and Klinger, Roman}, |
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title={Detecting miRNA Mentions and Relations in Biomedical Literature}, |
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journal={F1000Research}, |
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year={2014}, |
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month={Aug}, |
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day={28}, |
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publisher={F1000Research}, |
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volume={3}, |
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pages={205-205}, |
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keywords={MicroRNAs; corpus; prediction algorithms}, |
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abstract={ |
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INTRODUCTION: MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional |
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gene expression regulators, participating in a wide spectrum of regulatory events such as |
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apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal |
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physiology, their dysregulation is implicated in a vast array of diseases. Dissection of |
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miRNA-related associations are valuable for contemplating their mechanism in diseases, |
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leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy. |
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MOTIVATION: Apart from databases and prediction tools, miRNA-related information is largely |
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available as unstructured text. Manual retrieval of these associations can be labor-intensive |
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due to steadily growing number of publications. Additionally, most of the published miRNA |
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entity recognition methods are keyword based, further subjected to manual inspection for |
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retrieval of relations. Despite the fact that several databases host miRNA-associations |
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derived from text, lower sensitivity and lack of published details for miRNA entity |
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recognition and associated relations identification has motivated the need for developing |
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comprehensive methods that are freely available for the scientific community. Additionally, |
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the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the |
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available systems. We propose methods to automatically extract mentions of miRNAs, species, |
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genes/proteins, disease, and relations from scientific literature. Our generated corpora, |
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along with dictionaries, and miRNA regular expression are freely available for academic |
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purposes. To our knowledge, these resources are the most comprehensive developed so far. |
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RESULTS: The identification of specific miRNA mentions reaches a recall of 0.94 and |
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precision of 0.93. Extraction of miRNA-disease and miRNA-gene relations lead to an |
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F1 score of up to 0.76. A comparison of the information extracted by our approach to |
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the databases miR2Disease and miRSel for the extraction of Alzheimer's disease |
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related relations shows the capability of our proposed methods in identifying correct |
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relations with improved sensitivity. The published resources and described methods can |
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help the researchers for maximal retrieval of miRNA-relations and generation of |
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miRNA-regulatory networks. AVAILABILITY: The training and test corpora, annotation |
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guidelines, developed dictionaries, and supplementary files are available at |
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http://www.scai.fraunhofer.de/mirna-corpora.html. |
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}, |
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note={26535109[pmid]}, |
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note={PMC4602280[pmcid]}, |
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issn={2046-1402}, |
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url={https://pubmed.ncbi.nlm.nih.gov/26535109}, |
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language={eng} |
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} |
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""" |
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_DATASETNAME = "mirna" |
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_DISPLAYNAME = "miRNA" |
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_DESCRIPTION = """\ |
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The corpus consists of 301 Medline citations. The documents were screened for |
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mentions of miRNA in the abstract text. Gene, disease and miRNA entities were manually |
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annotated. The corpus comprises of two separate files, a train and a test set, coming |
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from 201 and 100 documents respectively. |
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""" |
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_HOMEPAGE = "https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html" |
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_LICENSE = 'Creative Commons Attribution Non Commercial 3.0 Unported' |
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_BASE = "https://www.scai.fraunhofer.de/content/dam/scai/de/downloads/bioinformatik/miRNA/miRNA-" |
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_URLs = { |
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"source": { |
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"train": _BASE + "Train-Corpus.xml", |
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"test": _BASE + "Test-Corpus.xml", |
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}, |
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"bigbio_kb": { |
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"train": _BASE + "Train-Corpus.xml", |
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"test": _BASE + "Test-Corpus.xml", |
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}, |
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} |
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_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION] |
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_SOURCE_VERSION = "1.0.0" |
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_BIGBIO_VERSION = "1.0.0" |
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class miRNADataset(datasets.GeneratorBasedBuilder): |
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"""mirna""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) |
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BUILDER_CONFIGS = [ |
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BigBioConfig( |
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name="mirna_source", |
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version=SOURCE_VERSION, |
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description="mirna source schema", |
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schema="source", |
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subset_id="mirna", |
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), |
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BigBioConfig( |
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name="mirna_bigbio_kb", |
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version=BIGBIO_VERSION, |
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description="mirna BigBio schema", |
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schema="bigbio_kb", |
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subset_id="mirna", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "mirna_source" |
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def _info(self): |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"passages": [ |
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{ |
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"document_id": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"offset": datasets.Value("int32"), |
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"entities": [ |
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{ |
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"id": datasets.Value("string"), |
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"offsets": [[datasets.Value("int32")]], |
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"text": [datasets.Value("string")], |
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"type": 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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) |
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elif self.config.schema == "bigbio_kb": |
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features = kb_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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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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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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my_urls = _URLs[self.config.schema] |
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path_xml_train = dl_manager.download(my_urls["train"]) |
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path_xml_test = dl_manager.download(my_urls["test"]) |
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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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"filepath": path_xml_train, |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": path_xml_test, |
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"split": "test", |
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}, |
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), |
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] |
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def _get_passages_and_entities(self, d) -> Tuple[List[Dict], List[List[Dict]]]: |
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sentences: List[Dict] = [] |
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entities: List[List[Dict]] = [] |
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relations: List[List[Dict]] = [] |
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text_total_length = 0 |
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po_start = 0 |
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for _, s in enumerate(d): |
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if s.attrib["text"] is None or len(s.attrib["text"]) <= 0: |
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continue |
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if len(s) <= 0: |
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continue |
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text_total_length += len(s.attrib["text"]) + 1 |
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po_end = po_start + len(s.attrib["text"]) |
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start = po_start |
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dp = { |
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"text": s.attrib["text"], |
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"type": "title" if ".s0" in s.attrib["id"] else "abstract", |
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"offsets": [(po_start, po_end)], |
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"offset": 0, |
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} |
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po_start = po_end + 1 |
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sentences.append(dp) |
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pe = [] |
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re = [] |
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for a in s: |
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if a.tag == "entity": |
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length = len(a.attrib["text"]) |
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if a.attrib["text"] is None or length <= 0: |
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continue |
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if a.attrib["type"] in ["MeSH_Indexing_Chemical", "OTHER"]: |
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continue |
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startOffset, endOffset = a.attrib["charOffset"].split("-") |
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startOffset, endOffset = int(startOffset), int(endOffset) |
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pe.append( |
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{ |
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"id": a.attrib["id"], |
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"type": a.attrib["type"], |
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"text": (a.attrib["text"],), |
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"offsets": [(start + startOffset, start + endOffset + 1)], |
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"normalized": [ |
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{"db_name": "miRNA-corpus", "db_id": a.attrib["id"]} |
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], |
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} |
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) |
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elif a.tag == "pair": |
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re.append( |
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{ |
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"id": a.attrib["id"], |
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"type": a.attrib["type"], |
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"arg1_id": a.attrib["e1"], |
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"arg2_id": a.attrib["e2"], |
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"normalized": [], |
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} |
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) |
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entities.append(pe) |
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relations.append(re) |
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return sentences, entities, relations |
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def _generate_examples( |
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self, |
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filepath: str, |
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split: str, |
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) -> Iterator[Tuple[int, Dict]]: |
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"""Yields examples as (key, example) tuples.""" |
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reader = ET.fromstring(open(str(filepath), "r").read()) |
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if self.config.schema == "source": |
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for uid, doc in enumerate(reader): |
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( |
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sentences, |
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sentences_entities, |
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relations, |
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) = self._get_passages_and_entities(doc) |
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if ( |
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len(sentences) < 1 |
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or len(sentences_entities) < 1 |
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or len(sentences_entities) != len(sentences) |
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): |
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continue |
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for p, pe, re in zip(sentences, sentences_entities, relations): |
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p.pop("offsets") |
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p["document_id"] = doc.attrib["id"] |
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p["entities"] = pe |
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yield uid, {"passages": sentences} |
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elif self.config.schema == "bigbio_kb": |
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uid = 0 |
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for idx, doc in enumerate(reader): |
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( |
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sentences, |
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sentences_entities, |
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relations, |
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) = self._get_passages_and_entities(doc) |
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if ( |
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len(sentences) < 1 |
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or len(sentences_entities) < 1 |
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or len(sentences_entities) != len(sentences) |
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): |
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continue |
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uid += 1 |
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entities = [e for pe in sentences_entities for e in pe] |
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for p in sentences: |
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p.pop("offset") |
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p["text"] = (p["text"],) |
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p["id"] = uid |
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uid += 1 |
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for e in entities: |
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e["id"] = uid |
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uid += 1 |
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relations = [r for re in relations for r in re] |
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for r in relations: |
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r["id"] = uid |
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uid += 1 |
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yield idx, { |
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"id": uid, |
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"document_id": doc.attrib["id"], |
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"passages": sentences, |
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"entities": entities, |
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"events": [], |
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"coreferences": [], |
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"relations": relations, |
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} |
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