What are common pitfalls or gotchas with text normalization?

Text normalization is a crucial process in natural language processing, but it comes with its own set of common pitfalls or gotchas. Understanding these can help you implement more effective text processing algorithms.

• Ignoring Case Sensitivity: Normalizing text without considering case can lead to different interpretations. For example, "Apple" and "apple" may represent the same concept, but treating them differently can skew analysis.
• Over-normalization: Sometimes, in the quest to unify text, important distinctions can be lost. For instance, turning all text to lowercase may eliminate the capacity to distinguish proper nouns.
• Non-standard Abbreviations: Normalizing abbreviations can introduce ambiguity. For example, "Dr." may refer to "Doctor" or "Drive," and without context, normalization could be misleading.
• Inconsistent Symbol Handling: Handling symbols like punctuation, emojis, or special characters inconsistently can lead to misinterpretation of the text semantics.
• Changes in Meaning: Certain normalization processes like stemming may alter words in a way that changes their meaning. For example, 'running' is stemmed to 'run,' which may not always be appropriate in all contexts.

By being aware of these pitfalls, you can better prepare your text normalization strategy to ensure it serves your overall objectives effectively.