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Is there any NLP library or package which can help in adding coma, punctuations, new line appropriately in text?

0

$begingroup$

I have movie transcript, where no coma, punctuations or new line. Is there any NLP technique which can help to implement this?

nlp preprocessing

share|improve this question

edited Apr 4 at 7:39

BenP

1685

asked Apr 4 at 6:27

Jhon PatricJhon Patric

185

$endgroup$

add a comment | 

0

$begingroup$

I have movie transcript, where no coma, punctuations or new line. Is there any NLP technique which can help to implement this?

nlp preprocessing

share|improve this question

edited Apr 4 at 7:39

BenP

1685

asked Apr 4 at 6:27

Jhon PatricJhon Patric

185

$endgroup$

add a comment | 

$begingroup$

I have movie transcript, where no coma, punctuations or new line. Is there any NLP technique which can help to implement this?

nlp preprocessing

share|improve this question

edited Apr 4 at 7:39

BenP

1685

asked Apr 4 at 6:27

Jhon PatricJhon Patric

185

$endgroup$

share|improve this question

edited Apr 4 at 7:39

BenP

1685

asked Apr 4 at 6:27

Jhon PatricJhon Patric

185

share|improve this question

edited Apr 4 at 7:39

BenP

1685

asked Apr 4 at 6:27

Jhon PatricJhon Patric

185

edited Apr 4 at 7:39

BenP

1685

edited Apr 4 at 7:39

BenP

1685

asked Apr 4 at 6:27

Jhon PatricJhon Patric

185

asked Apr 4 at 6:27

Jhon PatricJhon Patric

185

1 Answer
1

active

oldest

votes

0

$begingroup$

This can be solved with "text segmentation". NLP libraries have code for breaking given text into :

• Sentences


• Phrases


• Words

With this, you can break text into sentences and insert . or ? for each sentence. Similarly, dependency tree will help with inserting some punctuation marks (not all).

Example (breaking text into sentences):

import spacy
nlp = spacy.load('en_core_web_sm')
text = "I was expecting a surplus of cute close-ups but Burton does surprisingly little to win us over He's never been big on treacle but a bit more warmth in this chilly movie which barely follows the outline of the 1941 original would have gone a long way"
text_sentences = nlp(text)
for sentence in text_sentences.sents:
 print(sentence.text)

Output is :

I was expecting a surplus of cute close-ups but Burton does
surprisingly little to win us over

and

He's never been big on treacle but a bit more warmth in this chilly
movie which barely follows the outline of the 1941 original would have
gone a long way

More details : https://spacy.io/usage/linguistic-features

share|improve this answer

answered Apr 4 at 8:45

Shamit VermaShamit Verma

1,6391414

$endgroup$

add a comment | 

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0

$begingroup$

This can be solved with "text segmentation". NLP libraries have code for breaking given text into :

• Sentences


• Phrases


• Words

With this, you can break text into sentences and insert . or ? for each sentence. Similarly, dependency tree will help with inserting some punctuation marks (not all).

Example (breaking text into sentences):

import spacy
nlp = spacy.load('en_core_web_sm')
text = "I was expecting a surplus of cute close-ups but Burton does surprisingly little to win us over He's never been big on treacle but a bit more warmth in this chilly movie which barely follows the outline of the 1941 original would have gone a long way"
text_sentences = nlp(text)
for sentence in text_sentences.sents:
 print(sentence.text)

Output is :

I was expecting a surplus of cute close-ups but Burton does
surprisingly little to win us over

and

He's never been big on treacle but a bit more warmth in this chilly
movie which barely follows the outline of the 1941 original would have
gone a long way

More details : https://spacy.io/usage/linguistic-features

share|improve this answer

answered Apr 4 at 8:45

Shamit VermaShamit Verma

1,6391414

$endgroup$

add a comment | 

0

$begingroup$

This can be solved with "text segmentation". NLP libraries have code for breaking given text into :

• Sentences


• Phrases


• Words

With this, you can break text into sentences and insert . or ? for each sentence. Similarly, dependency tree will help with inserting some punctuation marks (not all).

Example (breaking text into sentences):

import spacy
nlp = spacy.load('en_core_web_sm')
text = "I was expecting a surplus of cute close-ups but Burton does surprisingly little to win us over He's never been big on treacle but a bit more warmth in this chilly movie which barely follows the outline of the 1941 original would have gone a long way"
text_sentences = nlp(text)
for sentence in text_sentences.sents:
 print(sentence.text)

Output is :

I was expecting a surplus of cute close-ups but Burton does
surprisingly little to win us over

and

He's never been big on treacle but a bit more warmth in this chilly
movie which barely follows the outline of the 1941 original would have
gone a long way

More details : https://spacy.io/usage/linguistic-features

share|improve this answer

answered Apr 4 at 8:45

Shamit VermaShamit Verma

1,6391414

$endgroup$

add a comment | 

$begingroup$

This can be solved with "text segmentation". NLP libraries have code for breaking given text into :

• Sentences


• Phrases


• Words

With this, you can break text into sentences and insert . or ? for each sentence. Similarly, dependency tree will help with inserting some punctuation marks (not all).

Example (breaking text into sentences):

import spacy
nlp = spacy.load('en_core_web_sm')
text = "I was expecting a surplus of cute close-ups but Burton does surprisingly little to win us over He's never been big on treacle but a bit more warmth in this chilly movie which barely follows the outline of the 1941 original would have gone a long way"
text_sentences = nlp(text)
for sentence in text_sentences.sents:
 print(sentence.text)

Output is :

I was expecting a surplus of cute close-ups but Burton does
surprisingly little to win us over

and

He's never been big on treacle but a bit more warmth in this chilly
movie which barely follows the outline of the 1941 original would have
gone a long way

More details : https://spacy.io/usage/linguistic-features

share|improve this answer

answered Apr 4 at 8:45

Shamit VermaShamit Verma

1,6391414

$endgroup$

import spacy
nlp = spacy.load('en_core_web_sm')
text = "I was expecting a surplus of cute close-ups but Burton does surprisingly little to win us over He's never been big on treacle but a bit more warmth in this chilly movie which barely follows the outline of the 1941 original would have gone a long way"
text_sentences = nlp(text)
for sentence in text_sentences.sents:
 print(sentence.text)

share|improve this answer

answered Apr 4 at 8:45

Shamit VermaShamit Verma

1,6391414

answered Apr 4 at 8:45

Shamit VermaShamit Verma

1,6391414

answered Apr 4 at 8:45

Shamit VermaShamit Verma

1,6391414