How to use Stanford Parser in NLTK using Python

Note that this answer applies to NLTK v 3.0, and not to more recent versions. Sure, try the following in Python: Output: [Tree(‘ROOT’, [Tree(‘S’, [Tree(‘INTJ’, [Tree(‘UH’, [‘Hello’])]), Tree(‘,’, [‘,’]), Tree(‘NP’, [Tree(‘PRP$’, [‘My’]), Tree(‘NN’, [‘name’])]), Tree(‘VP’, [Tree(‘VBZ’, [‘is’]), Tree(‘ADJP’, [Tree(‘JJ’, [‘Melroy’])])]), Tree(‘.’, [‘.’])])]), Tree(‘ROOT’, [Tree(‘SBARQ’, [Tree(‘WHNP’, [Tree(‘WP’, [‘What’])]), Tree(‘SQ’, [Tree(‘VBZ’, [‘is’]), Tree(‘NP’, [Tree(‘PRP$’, [‘your’]), Tree(‘NN’, [‘name’])])]), …

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Python code to remove HTML tags from a string 

Using a regex Using a regex, you can clean everything inside <> : Some HTML texts can also contain entities that are not enclosed in brackets, such as ‘&nsbm‘. If that is the case, then you might want to write the regex as This link contains more details on this. Using BeautifulSoup You could also use BeautifulSoup additional package to find …

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