What is the goal of tokenization in natural language processing?

The goal of tokenization in natural language processing (NLP) is fundamentally about breaking down text into smaller, manageable pieces, known as tokens, which can be words, phrases, or symbols. This is a crucial step in NLP as it allows the system to process and analyze language data effectively.

Option B emphasizes the importance of capturing maximum input while minimizing the number of tokens needed, which reflects a key objective in tokenization. By achieving a balance between detail and brevity, tokenization enables more efficient processing and ensures that relevant information is retained without unnecessary complexity. This efficiency is particularly vital for models to understand and generate text since it directly impacts the training and inference phases of natural language models.

Other options focus on different aspects of text processing. Simplifying text, translating languages, and enhancing readability do not directly address the intrinsic purpose of tokenization, which centers more around structuring input data rather than altering its original meaning or presentation. Thus, the emphasis on maximizing input while minimizing token count is what makes option B the correct choice regarding the specific goal of tokenization in NLP.