Created
April 24, 2020 07:05
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Extracting Facts from a datasets of information
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| import wikipedia | |
| import spacy | |
| import textacy.extract | |
| # f= open("facts.txt","a+") | |
| # Load the large English NLP model | |
| nlp = spacy.load('en_core_web_sm') | |
| # Detail of the searched word | |
| content =wikipedia.page("Apple Inc").content | |
| doc = nlp(content) | |
| # Extract semi-structured statements with "Apple" as the Head-Word | |
| statements = textacy.extract.semistructured_statements(doc, "Apple") | |
| # Print the results | |
| print("Here are the things I know about Apple:") | |
| for statement in statements: | |
| subject, verb, fact = statement | |
| print(f"-{fact}") | |
| # facts = f" - {fact}" | |
| # print(facts) | |
| # f.write(facts) | |
| # f.close() |
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