Wordnet antonyms

antonyms.py

#!/usr/bin/env python
# coding: utf-8

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get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')


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from nlg.utils import load_spacy_model
import pandas as pd
from nlg.search import templatize


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df = pd.read_csv('nlg/tests/data/actors.csv')
fh_args = {'_sort': ['-rating']}


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nlp = load_spacy_model()


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doc = nlp('James Stewart is the actor with the highest rating.')


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nugget = templatize(doc, fh_args, df)


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print(nugget)


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[(c.pos_, c.text) for c in doc]


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import lemminflect


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[(c.pos_, c._.lemma(), c.text) for c in doc]


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high = doc[-3]


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get_ipython().run_line_magic('pinfo', 'high._.get')


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lemminflect.getInflection('high', 'JJR')


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high


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high.tag_


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from spacy_wordnet.wordnet_annotator import WordnetAnnotator


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nlp.add_pipe(WordnetAnnotator(nlp.lang), after='tagger')


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doc = nlp(doc.text)


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get_ipython().run_line_magic('timeit', 'nlp(doc.text)')


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t = doc[-3]


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ants = [c.antonyms()[0] for c in t._.wordnet.lemmas() if len(c.antonyms()) > 0]


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[c.name() for c in ants]


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lemminflect.getInflection('low', 'JJS')


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type(ant)


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t._.wordnet.lemmas()


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