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import os |
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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{VANMULLIGEN2012879, |
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title = {The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships}, |
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journal = {Journal of Biomedical Informatics}, |
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volume = {45}, |
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number = {5}, |
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pages = {879-884}, |
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year = {2012}, |
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note = {Text Mining and Natural Language Processing in Pharmacogenomics}, |
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issn = {1532-0464}, |
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doi = {https://doi.org/10.1016/j.jbi.2012.04.004}, |
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url = {https://www.sciencedirect.com/science/article/pii/S1532046412000573}, |
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author = {Erik M. {van Mulligen} and Annie Fourrier-Reglat and David Gurwitz and Mariam Molokhia and Ainhoa Nieto and Gianluca Trifiro and Jan A. Kors and Laura I. Furlong}, |
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keywords = {Text mining, Corpus development, Machine learning, Adverse drug reactions}, |
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abstract = {Corpora with specific entities and relationships annotated are essential to train and evaluate text-mining systems that are developed to extract specific structured information from a large corpus. In this paper we describe an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface. The agreement figures achieved show that the inter-annotator agreement is much better than the agreement with the system provided annotations. The corpus has been annotated for drugs, disorders, genes and their inter-relationships. For each of the drug–disorder, drug–target, and target–disorder relations three experts have annotated a set of 100 abstracts. These annotated relationships will be used to train and evaluate text-mining software to capture these relationships in texts.} |
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} |
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""" |
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_DATASETNAME = "euadr" |
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_DISPLAYNAME = "EU-ADR" |
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_DESCRIPTION = """\ |
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Corpora with specific entities and relationships annotated are essential to \ |
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train and evaluate text-mining systems that are developed to extract specific \ |
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structured information from a large corpus. In this paper we describe an \ |
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approach where a named-entity recognition system produces a first annotation and \ |
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annotators revise this annotation using a web-based interface. The agreement \ |
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figures achieved show that the inter-annotator agreement is much better than the \ |
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agreement with the system provided annotations. The corpus has been annotated \ |
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for drugs, disorders, genes and their inter-relationships. For each of the \ |
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drug-disorder, drug-target, and target-disorder relations three experts \ |
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have annotated a set of 100 abstracts. These annotated relationships will be \ |
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used to train and evaluate text-mining software to capture these relationships \ |
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in texts. |
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""" |
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_HOMEPAGE = "https://www.sciencedirect.com/science/article/pii/S1532046412000573" |
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_LICENSE = 'License information unavailable' |
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_URL = "https://biosemantics.erasmusmc.nl/downloads/euadr.tgz" |
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_SOURCE_VERSION = "1.0.0" |
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_BIGBIO_VERSION = "1.0.0" |
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_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION] |
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class EUADR(datasets.GeneratorBasedBuilder): |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) |
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DEFAULT_CONFIG_NAME = "euadr_bigbio_kb" |
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BUILDER_CONFIGS = [ |
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BigBioConfig( |
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name="euadr_source", |
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version=SOURCE_VERSION, |
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description="EU-ADR source schema", |
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schema="source", |
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subset_id="euadr", |
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), |
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BigBioConfig( |
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name="euadr_bigbio_kb", |
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version=BIGBIO_VERSION, |
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description="EU-ADR simplified BigBio schema for named entity recognition and relation extraction", |
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schema="bigbio_kb", |
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subset_id="euadr", |
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), |
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] |
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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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"pmid": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"abstract": datasets.Value("string"), |
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"annotations": datasets.Sequence(datasets.Value("string")), |
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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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urls = _URL |
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datapath = 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={"datapath": datapath, "dl_manager": dl_manager}, |
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), |
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] |
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def _generate_examples(self, datapath, dl_manager): |
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def replace_html_special_chars(string): |
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for (i, r) in [ |
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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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(">", ">"), |
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(">", ">"), |
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("'", "'"), |
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]: |
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string = string.replace(i, r) |
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return string |
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def suppr_blank(l_str): |
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r = [] |
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for string in l_str: |
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if len(string) > 0: |
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r.append(string) |
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return r |
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folder_path = os.path.join(datapath, "euadr_corpus") |
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key = 0 |
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if self.config.schema == "source": |
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for filename in sorted(os.listdir(folder_path)): |
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if "_" not in filename: |
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corpus_path = dl_manager.download_and_extract( |
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f"https://pubmed.ncbi.nlm.nih.gov/{filename[:-4]}/?format=pubmed" |
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) |
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with open(corpus_path, "r", encoding="latin") as f: |
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full_html = replace_html_special_chars( |
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("".join(f.readlines())) |
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.replace("\r\n", "") |
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.replace("\n", "") |
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) |
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abstract = " ".join( |
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suppr_blank( |
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full_html.split("AB -")[-1] |
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.split("FAU -")[0] |
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.split(" ") |
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) |
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) |
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title = " ".join( |
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suppr_blank( |
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full_html.split("TI -")[-1].split("PG")[0].split(" ") |
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) |
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) |
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full_text = " ".join([title, abstract]) |
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with open( |
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os.path.join(folder_path, filename), "r", encoding="latin" |
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) as f: |
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lines = f.readlines() |
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yield key, { |
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"pmid": filename[:-4], |
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"title": title, |
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"abstract": abstract, |
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"annotations": lines, |
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} |
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key += 1 |
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elif self.config.schema == "bigbio_kb": |
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for filename in sorted(os.listdir(folder_path)): |
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if "_" not in filename: |
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corpus_path = dl_manager.download_and_extract( |
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f"https://pubmed.ncbi.nlm.nih.gov/{filename[:-4]}/?format=pubmed" |
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) |
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with open(corpus_path, "r", encoding="latin") as f: |
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full_html = replace_html_special_chars( |
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("".join(f.readlines())) |
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.replace("\r\n", "") |
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.replace("\n", "") |
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) |
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abstract = " ".join( |
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suppr_blank( |
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full_html.split("AB -")[-1] |
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.split("FAU -")[0] |
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.split(" ") |
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) |
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) |
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title = " ".join( |
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suppr_blank( |
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full_html.split("TI -")[-1].split("PG")[0].split(" ") |
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) |
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) |
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full_text = " ".join([title, abstract]) |
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with open( |
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os.path.join(folder_path, filename), "r", encoding="latin" |
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) as f: |
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lines = f.readlines() |
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data = { |
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"id": str(key), |
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"document_id": str(key), |
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"passages": [], |
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"entities": [], |
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"events": [], |
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"coreferences": [], |
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"relations": [], |
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} |
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key += 1 |
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data["passages"].append( |
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{ |
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"id": str(key), |
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"type": "title", |
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"text": [title], |
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"offsets": [[0, len(title)]], |
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} |
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) |
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key += 1 |
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data["passages"].append( |
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{ |
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"id": str(key), |
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"type": "abstract", |
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"text": [abstract], |
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"offsets": [ |
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[len(title) + 1, len(title) + 1 + len(abstract)] |
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], |
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} |
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) |
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key += 1 |
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for line in lines: |
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line_processed = line.split("\t") |
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if line_processed[2] == "relation": |
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data["entities"].append( |
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{ |
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"id": str(key), |
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"offsets": [ |
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[ |
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int(line_processed[7].split(":")[0]), |
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int(line_processed[7].split(":")[1]), |
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] |
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], |
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"text": [ |
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full_text[ |
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int( |
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line_processed[7].split(":")[0] |
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) : int(line_processed[7].split(":")[1]) |
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] |
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], |
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"type": "", |
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"normalized": [], |
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} |
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) |
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key += 1 |
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data["entities"].append( |
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{ |
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"id": str(key), |
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"offsets": [ |
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[ |
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int(line_processed[8].split(":")[0]), |
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int(line_processed[8].split(":")[1]), |
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] |
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], |
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"text": [ |
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full_text[ |
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int( |
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line_processed[8].split(":")[0] |
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) : int(line_processed[8].split(":")[1]) |
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] |
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], |
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"type": "", |
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"normalized": [], |
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} |
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) |
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key += 1 |
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data["relations"].append( |
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{ |
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"id": str(key), |
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"type": line_processed[-1].split("\n")[0], |
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"arg1_id": str(key - 2), |
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"arg2_id": str(key - 1), |
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"normalized": [], |
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} |
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) |
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key += 1 |
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elif line_processed[2] == "concept": |
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data["entities"].append( |
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{ |
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"id": str(key), |
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"offsets": [ |
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[ |
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int(line_processed[4]), |
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int(line_processed[5]), |
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] |
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], |
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"text": [ |
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full_text[ |
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int(line_processed[4]) : int( |
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line_processed[5] |
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) |
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] |
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], |
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"type": line_processed[-1].split("\n")[0], |
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"normalized": [], |
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} |
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) |
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key += 1 |
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yield key, data |
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key += 1 |
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