TF-IDF vs Word2Vec Usage

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Problem

Scenario

You are comparing two common text representation methods for an NLP system. One is a sparse term-based approach, and the other is a dense embedding approach that captures semantic similarity between words.

Question

What are TF-IDF and Word2Vec, and when would you use them?

What This Tests

Understanding of TF-IDF as a sparse lexical representation
Understanding of Word2Vec as a dense semantic embedding method
When to use each approach in text classification or retrieval
How tokenization and preprocessing affect both methods