How can I do sentence tokenization

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This is the code that I used for sent_tokenize

import nltk
from nltk.tokenize import sent_tokenize
sent_tokenize(comments1)
Dataset

And I used an array to get sentences one by one but it didn't work

Arr=sent_tokenize(comments1)
Arr
Arr[0]

And when I use Arr[1] this error come up

IndexError                                
Traceback (most recent call last) <ipython-input-27-c15dd30f2746> in <module>
----> 1 Arr[1]

IndexError: list index out of range

NLTK's sent_tokenize works on well-formatted text. I think you're looking for a regular expression:

import re

comments_str = "1,Opposition MP Namal Rajapaksa questions Environment Minister President Sirisena over Wilpattu deforestation issue\nbut he should remember that it all started with his dad and uncle and might be he was in a coma in that days \n3, Opposition on MP Namal Rajapaksa questions Environment Minister President Sirisena over Wilpattu deforestation issue\n4,Pawu meya ba meyage thathata oka deddi kiyana thibbane"
comments = re.split(r'(?:^\d+,)|(?:\n\d+,)', comments_str)
print(comments)

Outputs:

[
    '',
    'Opposition MP Namal Rajapaksa questions Environment Minister President Sirisena over Wilpattu deforestation issue\nbut he should remember that it all started with his dad and uncle and might be he was in a coma in that days ',
    ' Opposition on MP Namal Rajapaksa questions Environment Minister President Sirisena over Wilpattu deforestation issue',
    'Pawu meya ba meyage thathata oka deddi kiyana thibbane'

]

NLP, These tokenizers work by separating the words using punctuation and spaces. And as mentioned in the code outputs above, it does not discard the punctuation,​  The sent_tokenize function uses an instance of PunktSentenceTokenizer from the nltk.tokenize.punkt module, which is already been trained and thus very well knows to mark the end and begining of sentence at what characters and punctuation. Code #2: PunktSentenceTokenizer – When we have huge chunks of data then it is efficient to use it.


The default NLTK tokenizer doesn't recognise the sentences here because the final ponctuation is missing. You can add it yourself before each newline "\n".

For instance:

comments1 = comments1.replace("\n", ".\n")
tokens = sent_tokenize(comments1)
for token in tokens:
    print("sentence: " + token)

You get something like that (truncated for readability):

sentence: 1, Opposition MP Namal Rajapaksa questions Environment Ministe [...] Sirisena over Wilpattu deforestation issue.
sentence: 2, but he should remember that it all started with his dad and [...] a coma in that days .
sentence: 3, Opposition MP Namal Rajapaksa questions Environment Ministe [...] Sirisena over Wilpattu deforestation issue.
sentence: 4, Pawu meya ba ba meyage  [...] 
sentence: 5, We visited Wilpaththu in August 2013 These are some of the  [...] deforestation of Wilpattu as Srilankans .
sentence: 6, Mamath wiruddai .
sentence: 7, Yeah we should get together and do something.
sentence: B, Mama Kamathyi oka kawada hari wenna tiyana deyak .
sentence: 9, Yes Channa Karunaratne we should stand agaist this act dnt  [...] as per news spreading Pls .
sentence: 10, LTTE eken elawala daapu oya minissunta awurudu 30kin passe [...] sathungena balaneka thama manussa kama .
sentence: 11, Shanthar mahaththayo ow aththa eminisunta idam denna one g [...] vikalpa yojana gena evi kiyala.
sentence: 12, You are wrong They must be given lands No one opposes it W [...]

Sentence Tokenization, It does this by looking for the types of textual constructs that confuse the tokenizer and replacing them with single words. The sentence tokenizer will not split an  For accomplishing such a task, you need both sentence tokenization as well as words to calculate the ratio. Such output serves as an important feature for machine training as the answer would be numeric. Check the below example to learn how sentence tokenization is different from words tokenization.


Read the comments in the following.

# Standard sentence tokenizer.
def sent_tokenize(text, language='english'):
    """
    Return a sentence-tokenized copy of *text*,
    using NLTK's recommended sentence tokenizer
    (currently :class:`.PunktSentenceTokenizer`
    for the specified language).

    :param text: text to split into sentences
    :param language: the model name in the Punkt corpus
    """
    tokenizer = load('tokenizers/punkt/{0}.pickle'.format(language))
    return tokenizer.tokenize(text)


def tokenize(self, text, realign_boundaries=True):
    """
    Given a text, returns a list of the sentences in that text.
    """
    return list(self.sentences_from_text(text, realign_boundaries))

Since language='english' takes !, ?, . ... as the end of a sentence, it works to add comments1 = comments1.replace('\n', '. ') before sent_tokenize(comments1).

Your case is possibly dulicated as the nltk sentence tokenizer, consider new lines as sentence boundary

NLP Pipeline: Sentence Tokenization (Part 6) - Edward Ma, When I am in school, teacher teaches how we should write an article. The importance sentence will be placed in the first sentence most of the  Sentence tokenization is the process of splitting text into individual sentences. For literature, journalism, and formal documents the tokenization algorithms built in to spaCy perform well, since the tokenizer is trained on a corpus of formal English text.


What is Tokenization, Tokenization is essentially splitting a phrase, sentence, paragraph, or an entire text document into smaller units, such as individual words or terms  To perform sentence tokenization, we can use the re.split () function. This will split the text into sentences by passing a pattern into it. import re. text = """Founded in 2002, SpaceX’s mission is to enable humans to become a spacefaring civilization and a multi-planet.


nltk.tokenize package, Stores data used to perform sentence boundary detection with Punkt. abbrev_types = None¶. A set of word types for known abbreviations. A sentence or data can be split into words using the method word_tokenize(): from nltk.tokenize import sent_tokenize, word_tokenize data = "All work and no play makes jack a dull boy, all work and no play"


Tokenizing Words and Sentences with NLTK, It will download all the required packages which may take a while, the bar on the bottom shows the progress. Tokenize words. A sentence or data  For example, consider the sentence: “Never give up”. The most common way of forming tokens is based on space. Assuming space as a delimiter, the tokenization of the sentence results in 3 tokens – Never-give-up. As each token is a word, it becomes an example of Word tokenization. Similarly, tokens can be either characters or subwords.