What process involves splitting input data into smaller meaningful units?

What process involves splitting input data into smaller meaningful units?

• A. Normalization
• B. Feature Extraction
• C. Tokenization
• D. Data Augmentation

Answer: (C)

The process of splitting input data into smaller meaningful units is called tokenization. This technique is particularly relevant in the field of natural language processing (NLP), where text data needs to be broken down into smaller parts, such as words or phrases, to be effectively analyzed and processed by machine learning models. Tokenization allows the model to understand and work with the language at a level that is both manageable and meaningful, facilitating tasks like sentiment analysis, translation, or any task that requires text interpretation. In contrast, normalization is focused on scaling numerical data to a standard range, feature extraction involves selecting relevant features from the data to improve model performance, and data augmentation refers to techniques used to increase the diversity of training data by applying various transformations. These processes serve different purposes and are not primarily concerned with the act of breaking data into smaller, meaningful units like tokenization does.

The process of splitting input data into smaller meaningful units is called tokenization. This technique is particularly relevant in the field of natural language processing (NLP), where text data needs to be broken down into smaller parts, such as words or phrases, to be effectively analyzed and processed by machine learning models. Tokenization allows the model to understand and work with the language at a level that is both manageable and meaningful, facilitating tasks like sentiment analysis, translation, or any task that requires text interpretation.

In contrast, normalization is focused on scaling numerical data to a standard range, feature extraction involves selecting relevant features from the data to improve model performance, and data augmentation refers to techniques used to increase the diversity of training data by applying various transformations. These processes serve different purposes and are not primarily concerned with the act of breaking data into smaller, meaningful units like tokenization does.