While tokenization is a fundamental step in NLP, it also has some limitations. Ambiguous or context-dependent tokenization cases can arise, leading to tokenization errors. Certain linguistic phenomena, such as idiomatic expressions, can pose challenges for tokenizers. Tokenization may also result in information loss, especially when dealing with languages that heavily rely on context or when tokenization is applied aggressively.