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NLTK (Natural Language Toolkit) is a powerful library in Python that provides tools to work with human language data (text). It has modules for various tasks such as tokenization, stemming, and part-of-speech tagging, as well as many others.
One of the great things about NLTK is that it comes with a lot of corpora (large datasets) that you can use to train and test your models. Some examples of these include the Brown Corpus, which is a collection of text from a variety of sources, and the Penn Treebank, which is a set of treebanks (syntax trees) from the University of Pennsylvania.
Let's start by installing NLTK and downloading the necessary corpora.
!pip install nltk
import nltk
nltk.download('brown')
nltk.download('treebank')The nltk.download() function is used to download a particular resource from the NLTK data server. The brown resource is a collection of text from a variety of sources, and it is often used for testing and experimentation in natural language processing. The treebank resource is a set of treebanks (syntax trees) from the University of Pennsylvania, and it is often used for testing and experimentation in natural language processing.
Next, let's use NLTK to tokenize some text.
Tokenization is the process of breaking a piece of text into individual tokens (usually words).
from nltk.tokenize import word_tokenize
text = "This is an example of tokenization."
tokens = word_tokenize(text)
print(tokens)This will output the following:
['This', 'is', 'an', 'example', 'of', 'tokenization', '.']We can also use NLTK to stem words.
Stemming is the process of reducing a word to its base form (for example, reducing "jumping" to "jump").
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
stemmed_words = [stemmer.stem(word) for word in tokens]
print(stemmed_words)This will output the following:
['thi', 'is', 'an', 'exampl', 'of', 'token', '.']We can also use NLTK to perform part-of-speech tagging.
Part-of-speech tagging is the process of labeling each word in a piece of text with its part of speech (noun, verb, adjective, etc.).
from nltk.tag import pos_tag
tagged_tokens = pos_tag(tokens)
print(tagged_tokens)This will output the following:
[('This', 'DT'), ('is', 'VBZ'), ('an', 'DT'), ('example', 'NN'), ('of', 'IN'), ('tokenization', 'NN'), ('.', '.')]These are just a few examples of what you can do with NLTK. It's a very powerful library that can help you with many natural language processing tasks. I hope this gives you a good idea of how to get started with it!
