<jupyter_start><jupyter_text>WordNet Taxonomy- From<jupyter_code># https://www.guru99.com/wordnet-nltk.html

from nltk.corpus import wordnet as wn
from pprint import pprint

print('Synonyms of good: ')
pprint(wn.synsets("good"))

hyper = lambda s : s.hypernyms()

good = wn.synset('good.n.01')
print('\nHypernyms of good.n.01: ')
pprint(list(good.closure(hyper)))

good = wn.synset('good.n.02')
print('\nHypernyms of good.n.02: ')
pprint(list(good.closure(hyper)))

panda = wn.synset('panda.n.01')
print('\nHypernyms of panda: ')
pprint(list(panda.closure(hyper)))<jupyter_output>Synonyms of good: 
[Synset('good.n.01'),
 Synset('good.n.02'),
 Synset('good.n.03'),
 Synset('commodity.n.01'),
 Synset('good.a.01'),
 Synset('full.s.06'),
 Synset('good.a.03'),
 Synset('estimable.s.02'),
 Synset('beneficial.s.01'),
 Synset('good.s.06'),
 Synset('good.s.07'),
 Synset('adept.s.01'),
 Synset('good.s.09'),
 Synset('dear.s.02'),
 Synset('dependable.s.04'),
 Synset('good.s.12'),
 Synset('good.s.13'),
 Synset('effective.s.04'),
 Synset('good.s.15'),
 Synset('good.s.16'),
 Synset('good.s.17'),
 Synset('good.s.18'),
 Synset('good.s.19'),
 Synset('good.s.20'),
 Synset('good.s.21'),
 Synset('well.r.01'),
 Synset('thoroughly.r.02')]

Hypernyms of good.n.01: 
[Synset('advantage.n.01'),
 Synset('asset.n.01'),
 Synset('quality.n.01'),
 Synset('attribute.n.02'),
 Synset('abstraction.n.06'),
 Synset('entity.n.01')]

Hypernyms of good.n.02: 
[Synset('morality.n.01'),
 Synset('quality.n.01'),
 Synset('attribute.n.02'),
 Synset('abstraction.n.06'),
 Synset('entity.n.01')]

Hypernyms of p[...]
{'fraction_non_alphanumeric': 0.24227129337539433, 'fraction_numerical': 0.05804416403785489, 'mean_word_length': 5.147286821705427, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 1, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '261710', 'license': 'MIT', 'n_tokens_mistral': 750, 'n_tokens_neox': 693, 'n_words': 89, 'path': 'Terms-and-Abbreviations/WordNet_Taxonomy.ipynb', 'repo_name': 'gymk/ANLP', 'size': '1585'}