How can tokenization impact the accuracy of NLP models?
Here are several ways in which tokenization can impact the accuracy of these models: 1. Granularity of Tokens Word vs. Subword vs. Character Tokenization : The choice of tokenization method affects how the model interprets language. For instance, word tokenization may lose nuances in compound words or phrases, while subword tokenization (like Byte Pair Encoding or WordPiece) can handle rare words and morphological variations better. Character tokenization captures every detail but may lead to longer sequences that are harder for models to process effectively. Impact on Context : The granularity of tokens can influence how well the model understands context. For example, splitting "New York" into two tokens ("New" and "York") may lead to a loss of meaning, affecting the model's ability to understand references to the city. 2. Handling of Special Cases Punctuation and Special Characters : How a tokenizer handles punctuation, special characters, and w...