Text Preprocessing in AI & Artificial Intelligence

Raw text is messy. It contains emojis, HTML tags, and irregular casing that can confuse simple models. The Standardization process begins by converting all text to lowercase and removing Noise (punctuation, special characters). This ensures that the model sees 'Information' and 'information' as the same concept, effectively reducing the Vocabulary Size and focusing the model on semantic meaning rather than stylistic variation.

Tokenization is the process of breaking a continuous stream of text into discrete units called Tokens. While the most common method is splitting by whitespace (Word Tokenization), modern models often use Sub-word Tokenization to handle rare words or complex prefixes. This list of tokens is the fundamental input for almost every advanced NLP algorithm, from Bag-of-Words to Transformers.

To reduce word variance, we use normalization. Stemming is a crude heuristic that chops off word suffixes (e.g., 'caresses' becomes 'caress'); it's fast but can produce non-dictionary words. Lemmatization is more sophisticated, using a lexicon and morphological analysis to return the base dictionary form (the Lemma). While Lemmatization is computationally more expensive, it is essential for tasks where grammatical accuracy is critical.