Text normalization

Text normalization is a process by which text is transformed in some way to make it consistent in a way which it might not have been before. Text normalization is often performed before text is processed in some way, such as generating synthesized speech, automated language translation, storage in a database, or comparison.

Examples of text normalization:

• normalizing of Unicode
• converting all letters to lower or upper case
• converting numbers (dates, currencies, temperature) into words
• removing accent marks and other diacritics from letters
• removing punctuation
• expanding abbreviations
• removing stopwords or "too common" words
• stemming
• text canonicalization (replacing words with their full equivalents, e.g. "co-operation" → "cooperation", "valour" → "valor", "should've" → "should have")
• removing repeating characters ("I looooove it!" → "I love it!")

While this may be done manually, and usually is in the case of ad hoc and personal documents, many programming languages support mechanisms which enable text normalization. These tasks also are not to be performed with blunt regular expressions, in some cases it might require dictionary and other linguistic resources.

Text normalization is useful, for example, for comparing two sequences of characters which mean the same but are represented differently. It also is crucial for search engines and corpus management. The examples of this kind of normalization include, but not limited to, "don't" vs "do not", "I'm" vs "I am", "Can't" vs "Cannot". Further, "1" and "one" are the same, "1st" is the same as "first", and so on. Instead of treating these strings as different, through text processing, one can treat them as the same.