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10 Modules
Which of the following methods can be used for tokenization in NLP?
Whitespace Tokenization
Regular Expression Tokenization
Subword Tokenization
Character Tokenization
What is the main purpose of tokenization in NLP?
To convert text into a structured format
To remove stop words
To identify named entities
To segment text into smaller units
Which of the following is a potential issue with naive whitespace tokenization?
It cannot handle punctuation correctly
It may split words incorrectly
It is computationally expensive
It ignores special characters
In which scenario would subword tokenization be particularly beneficial?
When dealing with a large vocabulary
For languages with rich morphology
When the dataset is small
For text with many out-of-vocabulary words
What is the role of a tokenizer in the context of NLP?
To convert text into numerical representations
To segment text into tokens
To perform sentiment analysis
To identify parts of speech
10 Modules