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    Question

    Which natural language processing (NLP) technique is

    best suited for understanding the contextual meaning of words in a sentence?
    A Bag of Words (BoW) Correct Answer Incorrect Answer
    B One-Hot Encoding Correct Answer Incorrect Answer
    C Word2Vec Correct Answer Incorrect Answer
    D Term Frequency-Inverse Document Frequency (TF-IDF) Correct Answer Incorrect Answer
    E Transformers (e.g., BERT) Correct Answer Incorrect Answer

    Solution

    Transformers like BERT (Bidirectional Encoder Representations from Transformers) have revolutionized NLP by capturing contextual word representations. Unlike traditional techniques, BERT processes words in both their preceding and succeeding contexts, enabling nuanced understanding. 1. Contextual Embeddings: BERT generates embeddings that vary depending on the surrounding words, addressing issues like polysemy (e.g., "bank" as a financial institution vs. a riverbank). 2. Bidirectionality: By analyzing text in both directions, BERT captures deeper linguistic patterns and relationships. 3. Pretraining and Fine-Tuning: BERT is pretrained on vast corpora and fine-tuned for specific NLP tasks, making it versatile for applications like sentiment analysis, question answering, and translation. Why Other Options Are Incorrect: • A) Bag of Words: Ignores word order and context, treating sentences as a collection of words. • B) One-Hot Encoding: Fails to capture semantic relationships between words. • C) Word2Vec: Generates static word embeddings, lacking context sensitivity. • D) TF-IDF: Focuses on word importance across documents but overlooks word order and meaning.

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