What are the applications of tokenization in NLP?
An example of an AI chatbot conversation about NLP tokenization applications.
What are the applications of tokenization in NLP?
Tokenization is crucial in NLP for tasks like text analysis, sentiment analysis, and machine translation.
Can you explain how tokenization helps in sentiment analysis?
Sure! Tokenization breaks down text into words or phrases, allowing algorithms to analyze sentiment more effectively.
Are there different types of tokenization?
Yes, there are several types, including word tokenization, sentence tokenization, and subword tokenization.
How does tokenization impact machine learning models?
Tokenization transforms text into a format that machine learning models can understand, improving their performance.
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