How can I use NLTK for advanced tokenization?
An example of an AI chatbot conversation about using NLTK for advanced tokenization techniques.
How can I use NLTK for advanced tokenization?
You can use NLTK's word_tokenize and sent_tokenize functions for basic tokenization, but for advanced techniques, consider using regular expressions or the RegexpTokenizer class.
What are some examples of advanced tokenization techniques?
Some advanced techniques include using custom regular expressions to match specific patterns, handling contractions, and tokenizing based on part-of-speech.
Can you show me a code example for using RegexpTokenizer?
Sure! Here's a simple example:
from nltk.tokenize import RegexpTokenizer
tokenizer = RegexpTokenizer(r'
\w+')
tokens = tokenizer.tokenize('Hello, world! This is NLTK.')
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