My Motivations I'm trying to learn German and realized there's a confounding fact with the structure of German: every noun has a gender which seems unrelated to the noun itself in many cases.

Unlike languages such as English, each noun has a different definite article, depending on gender: der (masculine), die (feminine), and das (neuter). For example: das Mädchen ("the girl"), der Rock ("the skirt), die Hose ("the trousers/pants"). So, there seems to be no correlation between gender assignment of nouns and their meanings.

The Data I gathered up to 5000 German words with 3 columns (das, der, die) for each word with 1's and 0's. So, my data is already clustered with one hot encoding and I'm not trying to predict anything.

Why I'm here I am clueless on where to start, how to approach this problem as the concept of distance in clustering doesn't make sense to me in this setting. I can't think of a way to generate an understandable description of these clusters. The mixed data makes it impossible for me to think of some hard-coded metrics for evaluation.

So, my question is: I want to find some patterns, some characteristics of these words that made them fall in a specific cluster. I don't know if I'm making any sense but some people managed to find some patterns already (for example word endings, elongated long objects tend to be masculine etc., etc) and I believe ML/AI could do a way better job at this. Would it be possible for me to do something like this?

Some personal thoughts While I was doing some research (perhaps, naive), I realized the potential options are decision trees and cobweb algorithms. Also, I was thinking if I could just scrape a few images (say 5) for every word and try to run some image classification and see the intermediate NN's to see if any specific shapes support a specific object gender. In addition to that, I was wondering whether scraping the data of google n-gram viewers of these words could help in anyway. I couldn't think of a way to use NLP or its sub domains.

Alternatives If everything I just wrote sounds nonsensical, please suggest me a way to make visual representations of my dataframe (more like nodes and paths with images at nodes, one for each cluster) in Python so that I could make pictorial mind maps and try to by heart them.

The ultimate purpose is to make learning German simpler for myself and possibly for others

Edit 1 : This idea of who sets a gender actually makes me think that if I could approach this problem in this perspective, I might actually have a better chance of being successful with my problem statement. Basically, using genders my multiple languages and by using longer timeframes.

  • It would help to know something about the grammatical gender vrom a languagelearning perspective, but that's out of scope for this Stack. Native speakers would have a hard explaining any rule and I don't know how we suck it up with the mothermilk--just that we do. Natives would advise that it has to be learned simply as pairs, but that's not entirely true if -chen for example always declines neuter (I see I'm not telling you anything new, you did some research). "The ultimate purpose is to make learning German simpler"--I'm not sure that matters to the question; at worst it looks ridic. xD – vectory Jun 8 '20 at 11:31
  • Have you read this? linguistics.stackexchange.com/questions/35863/… Conceivably, a Neural Net could train on recognizing morphemes that positively correlate with gender. Ideally you get different dimensions from the same ending (that may have different gender, e.g. n. or fem.) depending on what's inbetween, without even supplying segmentation. In practice, I wouldn't know where to start either, haha lol wut. Maybe it would only show that is indeed mostly arbitrary, indeed, I reall dunno – vectory Jun 8 '20 at 11:40
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    By now you will have noticed that not only does every German noun have a gender, it also has a plural ending, which is different from one noun to another. Unlike English, there is quite a choice of plural endings in German. In fact, these correlate (a bit) with gender, which is good from your point of view. Unfortunately, while native speakers can assign gender consistently to nonsense words by their shape, this is not possible for learners, and the plural and gender of German nouns pretty much has to be learned along with the spelling and meaning. The pronunciation is rule-governed, at least. – jlawler Jun 8 '20 at 15:22
  • If the correlated qualities can be extracted from/retrieved with the noun (e.g. by network action, or like via local process or api call etc), then something can be made, but hard to say whether it'd be meaningful; like said above, you might simply find that it's too unpredictable. Manifold models might help you find abstract correlates; I'd probably start there. Unsupervised ai is limited only by your imagination (and computational ability), but there are often anthropocentric leaps in people's expectations (e.g. correlate 'feminine' has prior dependence on ability to distinguish femininity). – TheLoneDeranger Jun 8 '20 at 19:05
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    One fact to show that grammatic gender can be conceptually detached from the word form are those cases where loan words assume the gender of the synonym, or what's logical; however, we also see that work its way back to agree with morphollogy where applicable (e.g. das > der Comput-er, later das Laptop > der Laptop(-Computer), but das Notebook (~*das Buch*)). In cases where the morphology plays no role, you can only hope that there's no incidental correlation, so as to be statisticly insignificant. Anyway, a comment in the linked thread pointed out that it works on morphemes, too. – vectory Jun 10 '20 at 20:34

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