An Advanced Profanity Filter

empowered by Artificial Intelligence AI

See if ObsceneClean version 1.2 beta (a profanity filter) can bust you! Unlike other profanity filters (or "swear" filters), ObsceneClean has extensive and Novel Natural Language processing (NLP) logic, a type of AI, to deal with the inevitable flood of false positives that plague other profanity filters that rely on simple comparison logic. The technical approach is to mirror human perception and interpretation with respect to offensive English text especially hate speech. ObsceneClean employs many techniques including shallow word sense disambiguation to determine the context of a potentially offensive word. Try it! The detection logic will be displayed. You can also try false positives. If you think ObsceneClean did not detect offensive language or you think it wrongfully claimed a false positive was offensive (see examples below) then email us at scott@mindofscott.com. By default a probability of 80% or greater is considered offensive.

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WARNING: Some may find content below offensive.

Any profanity filter worth its salt will deal with false positives well! The examples below are real world examples of homonyms that could cause a profanity filter to report a false positive. ObsceneClean looks at the meaning of words to determine what is offensive and what is not.

Have a fag

In the UK cigarettes are commonly called fags

Pork Faggots

Pork Faggots are meatballs in the UK

Become a Kunt

They were not trying to be funny

"Bitch" is commonly used at dog shows.

A real headline

Found in every household at one time

It really exists

Bungholes are used to plug kegs

This lure is rather common

A fine company that cares about quality

Bitches are female dogs

Many pubs with this name

Gook is a common name in Korea and infrequently in Scotland

Both macaca and nigra may be offensive depending on context

Pronounced "poo-khet"

Shag carpeting

Fortunately, residents renamed this Austrian village to Fugging