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import csv |
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import json |
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import datasets |
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_CITATION = """\ |
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@inproceedings{heindorf2020causenet, |
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author = {Stefan Heindorf and |
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Yan Scholten and |
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Henning Wachsmuth and |
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Axel-Cyrille Ngonga Ngomo and |
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Martin Potthast}, |
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title = CauseNet: Towards a Causality Graph Extracted from the Web, |
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booktitle = CIKM, |
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publisher = ACM, |
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year = 2020 |
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} |
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""" |
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_DESCRIPTION = """\ |
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Crawled Wikipedia Data from CIKM 2020 paper |
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'CauseNet: Towards a Causality Graph Extracted from the Web.' |
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""" |
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_URL = "https://github.com/causenet-org/CIKM-20" |
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_TRAIN_DOWNLOAD_URL = "https://groups.uni-paderborn.de/wdqa/causenet/causality-graphs/extraction/wikipedia/wikipedia-extraction.tsv" |
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_DEMO_DOWNLOAD_URL = "https://drive.google.com/uc?export=download&id=11gKbdn77ngBJr2C1iCK-YUDu9mWbXWx2" |
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class CauseNetWikiCorpus(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features({ |
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"cause_word": datasets.Value("string"), |
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"cause_id": datasets.Value("int64"), |
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"effect_word": datasets.Value("string"), |
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"effect_id": datasets.Value("int64"), |
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"pattern": datasets.Value("string"), |
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"sentence": datasets.Value("string"), |
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"dependencies": datasets.Value("string") |
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} |
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), |
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homepage=_URL, |
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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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train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL) |
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demo_path = dl_manager.download_and_extract(_DEMO_DOWNLOAD_URL) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}), |
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datasets.SplitGenerator(name="demo", gen_kwargs={"filepath": demo_path}) |
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] |
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def is_valid_article(self, title): |
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forbidden_title_parts = ['Wikipedia:', 'Template:', 'File:', |
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'Portal:', 'Category:', 'Draft:', |
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'List of', 'disambiguation'] |
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contains_forbidden_title_part = False |
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for forbidden_title_part in forbidden_title_parts: |
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if forbidden_title_part in title: |
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contains_forbidden_title_part = True |
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break |
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return not contains_forbidden_title_part |
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def _generate_examples(self, filepath): |
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""" |
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Generate examples. |
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We are reading csv files with the following columns: sentenceID | gold_label | sentence. |
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""" |
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for id_, line in enumerate(open(filepath, encoding="utf-8")): |
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parts = line.strip().split('\t') |
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if parts[0] != 'wikipedia_sentence': |
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continue |
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assert len(parts) == 11 |
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if not self.is_valid_article(parts[2]): |
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continue |
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for match in json.loads(parts[10]): |
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sentence_data = { |
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"cause_word": match['Cause'][0], |
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"cause_id": match['Cause'][1], |
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"effect_word": match['Effect'][0], |
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"effect_id": match['Effect'][1], |
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"pattern": match['Pattern'], |
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"sentence": json.loads(parts[7]), |
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"dependencies": json.loads(parts[9]) |
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} |
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yield id_, sentence_data |
