I have a list of words, belonging to different selfdefined categories. Each category has its own pattern (for example one has a fixed length with special characters, another exists of characters which occur only in this category of "word", ...).
"ABC" -> type1 "ACC" -> type1 "a8 219" -> type2 "c 827" -> type2 "ASDF 123" -> type2 "123123" -> type3 ...
I am searching for a machine learning technique to learn these pattern on its own, based on training data. I already tried to define some predictor variables (for example wordlength, number of special characters, ...) on my own and then used a Neural-Networks to learn and predict the category. But thats acutally not what i want. I want a technique to learn the pattern for each category on its own - even to learn patterns which I never thought about.
So i give the algorithm learning data (consisting of the word-category examples) and want it to learn patterns for each category to predict later the category from similar or equal words.
Is there a state-of-the-art way to do it?
Thanks for your help