What are the drawbacks of tokenization in NLP?
An example of an AI chatbot conversation about the drawbacks of tokenization in NLP.
What are the drawbacks of tokenization in NLP?
Tokenization can lead to loss of context, especially with complex phrases or idioms. It may also create ambiguity.
Can you give an example of how context is lost?
Sure! For instance, the phrase 'New York' might be split into 'New' and 'York', losing its meaning as a single entity.
Are there any specific languages where tokenization is particularly challenging?
Yes, languages like Chinese and Japanese are challenging because they don't use spaces between words, complicating tokenization.
What are some alternatives to tokenization?
Alternatives include subword tokenization methods like Byte Pair Encoding (BPE) or using character-level models.
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