Data Interview Question
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Requirements Clarification & Assessment
To effectively answer the question about bigram tokenization, it's important to understand:
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Definition of Bigrams: Clarify what bigrams are and how they differ from other n-grams, such as unigrams or trigrams.
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Purpose: Understand why bigram tokenization is used in natural language processing (NLP) and what advantages it offers over single-word tokenization.
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Applications: Identify the contexts or scenarios in which bigram tokenization is particularly beneficial, such as text classification, sentiment analysis, and language modeling.
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Implementation: Consider how bigram tokenization is implemented in practice, including any computational considerations or tools commonly used.
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Limitations and Trade-offs: Acknowledge any inherent limitations or trade-offs when using bigram tokenization compared to other methods.