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Update SemEval2018_Task7.py
Browse files- SemEval2018_Task7.py +2 -27
SemEval2018_Task7.py
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# I am trying to understand to the following code. Do not use this for any purpose as I do not support this.
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# Use the original source from https://huggingface.co/datasets/DFKI-SLT/SemEval2018_Task7/blob/main/SemEval2018_Task7.py
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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import xml.dom.minidom
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import xml.etree.ElementTree as ET
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@inproceedings{gabor-etal-2018-semeval,
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title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
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@@ -52,7 +47,6 @@ _CITATION = """\
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}
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"""
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# You can copy an official description
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_DESCRIPTION = """\
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This paper describes the first task on semantic relation extraction and classification in scientific paper
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abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three
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@@ -63,10 +57,8 @@ limited to scientific or bio-medical information extraction. The task attracted
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with 158 submissions across different scenarios.
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"""
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# Add a link to an official homepage for the dataset here
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_HOMEPAGE = "https://github.com/gkata/SemEval2018Task7/tree/testing"
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# Add the licence for the dataset here if you can find it
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_LICENSE = ""
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# Add link to the official dataset URLs here
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)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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features=features, # Here we define them above because they are different between the two configurations
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# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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# specify them. They'll be used if as_supervised=True in builder.as_dataset.
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# supervised_keys=("sentence", "label"),
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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if arg_id not in text_id_to_relations_map:
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text_id_to_relations_map[arg_id] = [relation]
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else:
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text_id_to_relations_map[arg_id].append(relation)
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#print("result", text_id_to_relations_map)
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#for arg_id, values in text_id_to_relations_map.items():
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#print(f"ID: {arg_id}")
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# for value in values:
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# (value)
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doc2 = ET.parse(text_filepath)
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root = doc2.getroot()
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if child.find("title")==None:
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continue
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text_id = child.attrib
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#print("text_id", text_id)
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if child.find("abstract")==None:
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continue
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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import xml.dom.minidom
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import xml.etree.ElementTree as ET
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_CITATION = """\
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@inproceedings{gabor-etal-2018-semeval,
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title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
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}
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"""
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_DESCRIPTION = """\
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This paper describes the first task on semantic relation extraction and classification in scientific paper
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abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three
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with 158 submissions across different scenarios.
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"""
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_HOMEPAGE = "https://github.com/gkata/SemEval2018Task7/tree/testing"
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_LICENSE = ""
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# Add link to the official dataset URLs here
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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=_LICENSE,
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citation=_CITATION,
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)
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if arg_id not in text_id_to_relations_map:
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text_id_to_relations_map[arg_id] = [relation]
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else:
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text_id_to_relations_map[arg_id].append(relation)
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doc2 = ET.parse(text_filepath)
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root = doc2.getroot()
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if child.find("title")==None:
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continue
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text_id = child.attrib
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if child.find("abstract")==None:
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continue
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