What are the main challenges of tokenization in AI?
Tokenization in AI faces several challenges, including handling out-of-vocabulary words, context preservation, and language diversity.
Can you explain how out-of-vocabulary words affect tokenization?
Out-of-vocabulary words can lead to loss of information, as they may be replaced with placeholders or split into subwords, affecting understanding.
What strategies can be used to improve tokenization?
Strategies include using subword tokenization, context-aware models, and expanding vocabulary through continuous learning.
How does tokenization impact natural language processing tasks?
Tokenization is crucial as it affects the input representation, which can influence the performance of NLP tasks like translation and sentiment analysis.
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