Hello Friends,
In previous post, we have seen how to install NLTK? In this post we start with How to work with NLTK?
As we know, NLTK is a library in python to work with NLP. So, First of all what is NLP?
NLP - Natural Language Processing is to develop an application to understand Human Languages.
some examples,
So, now let's start to work with NLTK.
Let's tokenize sentences,
Tokenize Sentence means we have a paragraph, and we split this paragraph into sentences. this process is known as tokenize sentences
Example,
from nltk.tokenize import sent_tokenize
sentence="This is an example of NLTK. Let's start with sentences tokenizer"
print(sent_tokenize(sentence))
O/P of above code
['This is an example of NLTK.', "Let's start with sentences tokenizer"]
Run the above code it will return list of sentences.
Now, Let's see an example of word_tokenize. Like tokenize sentences return different sentence in list from group of sentences.
word_tokenize returns list of words from sentences it splits words.
Example,
from nltk.tokenize import word_tokenize
sentence="This is an example of NLTK. Let's start with sentences tokenizer"
print(word_tokenize(sentence))
O/P of below code
['This', 'is', 'an', 'example', 'of', 'NLTK', '.', 'Let', "'s", 'start', 'with', 'sentences', 'tokenizer']
So, Start with NLTK and try to tokenize sentences and words. in next post we will learn more in NLTK.
Happy Coding... :)
In previous post, we have seen how to install NLTK? In this post we start with How to work with NLTK?
As we know, NLTK is a library in python to work with NLP. So, First of all what is NLP?
NLP - Natural Language Processing is to develop an application to understand Human Languages.
some examples,
- Amazon Alexa
- Google HomeMini, etc.
Some of it's application, Speech recognition, speech translation, understanding sentences, synonyms etc.
Where NLP is used?
- Search Engine - in search engine we have seen, we speak a word and it answers. like google, yahoo etc, uses NLP.
- Social Website Feeds - in social website like facebook uses news feeds.
- Spam Filters - Google filters spam mails.
These all are the examples of NLP.
So, now let's start to work with NLTK.
Let's tokenize sentences,
Tokenize Sentence means we have a paragraph, and we split this paragraph into sentences. this process is known as tokenize sentences
Example,
from nltk.tokenize import sent_tokenize
sentence="This is an example of NLTK. Let's start with sentences tokenizer"
print(sent_tokenize(sentence))
O/P of above code
['This is an example of NLTK.', "Let's start with sentences tokenizer"]
Run the above code it will return list of sentences.
Now, Let's see an example of word_tokenize. Like tokenize sentences return different sentence in list from group of sentences.
word_tokenize returns list of words from sentences it splits words.
Example,
from nltk.tokenize import word_tokenize
sentence="This is an example of NLTK. Let's start with sentences tokenizer"
print(word_tokenize(sentence))
O/P of below code
['This', 'is', 'an', 'example', 'of', 'NLTK', '.', 'Let', "'s", 'start', 'with', 'sentences', 'tokenizer']
So, Start with NLTK and try to tokenize sentences and words. in next post we will learn more in NLTK.
Happy Coding... :)
