Analyzing rules of articles in languages I'm interested in finding a solution for the following problem:


problem space: any language that has more than 1 article
let's take German language as example. So the articles in German are "der", "die", "das". - every word has an article as prefix and you must use the article. for example you can't say just "Auto"(car), you must say "das Auto".
If you are not a native German speaker, you have bad luck, because you have to memorize the article too, when learning a new word, since there are only 3-4 rules to know which word takes which article. And those rules are perhaps about max. 10% percent of the vocabulary. (for example if a word ends with "-ung", it takes "die")



So here comes the funny part: as a non-native-German-speaker, i wanted to analyze the language from an IT-point of view and asked some friends of mine random words with the following properties: 


first I reduced the input set from "any German word" to "a German word", for which no known article rule exists".
then I extended the input set with "made up words", which do not exist.


Every candidate had the same answer for German words, which should be not surprise. But when I asked them per word "why do you think/feel that the article of the word is "der/die/das"?" they couldnt give an answer. They just know it, without knowing why.

Here comes the real hammer: every candidate had the same answer for any made-up non-German word. I say anything, make any meaningful voice, and they all give the same answer (ie. article).

I'm pretty sure that I'm not the first human being in the world who made thoughts about this topic. And I'm interested in any scientific papers/research about that topic.
By the way, what I definitely do NOT need is, any method (like neural networks) which outputs a pure mathematical function. I thought about following possibilities:


focusing on feelings of subjects psychologically (I think it can be managed with colors somehow etc.)
analyzing the words statistically according to their syllable structure


Where can I begin? What are your suggestions? Are there any Machine Learning research about that topic?
A language is a result of a complex historical development. If the development were guarded by simple language generating rules it would've been different like some artificial languages such as the Esperanto language.

Many natural languages have nouns split into several categories. These categories are often referred to as the Gender. Though the link between the grammatical gender and the natural or biological gender is not always obvious.

In Slavic languages for instance the gender of a word can be guessed from the flection at the end of the word. E.g. in Russian "облако" (a cloud) is neutral, "-о" is the flection, and "зима" (the Winter) is femenine, "-а" is the flection.

In Old English the category gender did exist but was eliminated during the evolution towards the Modern English. In general all languages that stem from the Latin language inherited some form of grammatical gender (for more details see the discussion in Wikipedia).

When learning the language one has to memorize at least the words and there meaning. In learning languages that keep grammatical gender one has to memorize the gender as well. You can find the gender of a word in any German dictionary.

Look at the small letter f, m, or n, printed in italics next to the word.

E.g. 

Bündel, n a bundle; 

Bus, m a bus;

Tasse, f a cup;

That is "Das Bündel", and "Der Bus", and "Die Tasse".

When a comprehensive dictionary is available for a language with all the nouns annotated with their respective grammatical gender, I guess there is little space left for machine learning. It is much more easy to keep the complete dictionary in memory than to devise a set of rules that would always be prone to errors.