Imagine two different systems are used to prepare the text 'unhappiness' for a language model. System A breaks it into one unit: `['unhappiness']`. System B breaks it into two units: `['un', 'happiness']`. Briefly explain one potential advantage of System B's approach over System A's approach, particularly when the model needs to process text it has not seen before.

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In Natural Language Processing (NLP), tokens are considered the fundamental units of text, produced through a process called tokenization. Although the terms 'token' and 'word' are frequently used interchangeably in discussions, it is important to recognize that they have closely related yet distinct meanings within the field.

Distinction and Interchangeability of 'Tokens' and 'Words' in NLP

Consider the sentence: "The model's predictions weren't accurate." When this text is prepared for processing, which statement best analyzes the relationship between the concepts of 'words' and 'tokens'?

Given the following scenario, analyze why a text processing system would likely produce a higher count of 'tokens' than 'words' for the provided sentence. Support your analysis with specific examples from the text.

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