βπ -π ππ ππ π₯ππππ£ π£ππππ₯ ππππ π¨π π¦ππ ππ§ππ£ π¦π€π π₯πππ€π ππππ πͺπππ ππ ππ₯ π§ππ£ππππ₯π€. πππ π¨π π£π€π₯ π₯ππππ, ππ€ ππ πͺπ π¦ ππ πππͺ ππ π£π π π βπβ πππ πͺπ π¦ πππ§π ππππ£πππ₯ππ£π€ ππππ π₯πππ€ ππ πͺπ π¦π£ πππ‘π¦π₯, πͺπ π¦π£ π₯ππ©π₯ ππππ πππ€ ππ ππ‘πππ₯πππͺ π¦ππ£πππππππ.
We also find that text like this is incredibly commonβ-βparticularly on social media.
Another pain-point comes from diacritics (the little glyphs in Γ, Γ©, Γ ) that you'll find in almost every European language.
These characters have a hidden property that can trip up any NLP modelβ-βtake a look at the unicode for two versions of Γ:
Latin capital letter C with cedilla: \u00C7
Latin capital letter C + combining cedilla: \u0043\u0327
Both are completely different, despite rendering as the same character.
To deal with all of these text variants we need to use unicode normalization - which we will cover in this video.
Top comments (1)
This topic is briefly covered in this article: devopedia.org/text-normalization
In particular, check out the 4 forms: NFD, NFC, NFKD and NFKC