Answer: To break text into smaller units for processing

Tokenization is a fundamental preprocessing step in natural language processing (NLP) that involves breaking down text into smaller, manageable units called tokens. These tokens can be words, subwords, characters, or other meaningful segments depending on the specific tokenization strategy employed. **Why Option C is Correct:** - Tokenization transforms raw text into discrete elements that machine learning models can process effectively. - It serves as the foundation for various NLP tasks by converting unstructured text into structured data. - Common tokenization approaches include word-based tokenization (splitting by whitespace/punctuation), subword tokenization (used in models like BERT and GPT), and character-based tokenization. **Why Other Options Are Incorrect:** - **Option A (To encrypt text data):** Encryption is a security technique for protecting data confidentiality, not an NLP preprocessing step. Tokenization in NLP is about segmentation, not cryptographic transformation. - **Option B (To compress text files):** Compression reduces file size for storage or transmission efficiency, which is unrelated to the linguistic analysis purpose of tokenization in NLP pipelines. - **Option D (To translate text between languages):** Machine translation is a specific NLP application that may use tokenization as an initial step, but tokenization itself doesn't perform translation. Translation requires additional layers like sequence-to-sequence models with attention mechanisms. **Best Practices Context:** In AWS AI/ML services, tokenization is implicitly handled in services like Amazon Comprehend (for entity recognition, sentiment analysis) and Amazon SageMaker (when using built-in algorithms or bringing custom models). Proper tokenization ensures models receive clean, standardized input, which is crucial for achieving accurate results in text classification, named entity recognition, and other NLP tasks.

Author: LeetQuiz Editorial Team