CODEWITHSUNDEEP
pythonnlpspacy
Zainab Amjad
Zainab Amjad

Reputation: 13

Extracting Verb Phrase from Sentence

I am trying to extract verb and verb phrases from a column containing sentences for this purpose i have created a function called tagging. Here's my code:

pattern = [{'POS':'VERB'}]
def tagging(txt):
  verb_phrases = textacy.extract.matches(txt, patterns=pattern)
  return (verb_phrases)

and then i am applying this function a column from my dataset

dataset['Verbs'] = dataset['Sentences'].apply(lambda x: tagging(x))
dataset['Verbs']

But instead of returning me verbs, the output is like:

0      <generator object matches at 0x7f8eb98a5258>
1      <generator object matches at 0x7f8eb97df6d0>
2      <generator object matches at 0x7f8eb97df728>
3      <generator object matches at 0x7f8eb97df570>
4      <generator object matches at 0x7f8eb97df678>

Upvotes: 1

Views: 2020

Answers (2)

Dhruvan Ganesh
Dhruvan Ganesh

Reputation: 1570

Going through the docs of textacy.extract.matches, this function does not return but rather yields a Span and that's why you get generators.

    Yields:
        Next matching ``Span`` in ``doc``, in order of appearance

A common way of unrolling is list comprehension ([generator] / list(generator)) or iterating through it (for item in generator:)

For your case:

for verb_span in textacy.extract.matches(txt, patterns=pattern):
    print(verb_span)

verb_list = [verb_span for verb_span in textacy.extract.matches(txt, patterns=pattern)]

Upvotes: 1

paradocslover
paradocslover

Reputation: 3294

Try this:

pattern = [{'POS':'VERB'}]
def tagging(txt):
  verb_phrases = textacy.extract.matches(txt, patterns=pattern)
  return list(verb_phrases)

Upvotes: 0

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