Awkwords

Since we’re apparently talking about made-up words today, what about a look at another conlang tool?

If you’d like something a bit more hands-on than the all-in-one-power of Vulgar, Awkwords is one of several word generators that create words based on patterns of syllable vowel and consonant construction.

It uses template construction with patterns and sub-patterns. It can get fancy with probability weights and filtering. By default it uses the standard vowel, consonant patterning, but the labels are arbitrary, so you can build any syllable construction that you like.

With the existence of fully-formed language generators, tools like Awkwords are now most useful when you’re trying to generate words with specific patterns or rhythms. A bit of linguistic knowledge helps, to know what the possibilities are: like most generative tools, word generators are an encoding of expert knowledge. They’re a description of the internal workings of a thing, a living diagram.

In this case, the encoded knowledge is about patterns in syllables. You’ll have to supply the phonemes yourself, and you’ll need to work out what the possible patterns are. But the strength of a generative tool is that you don’t need to know all of this up front: you can experiment. Each run of the tool gives you feedback for how well you understand the problem you are trying to solve. Generativity allows you to course-correct.

Gen (& Sound Change Applier)

It’s easy to generate random combinations of letters, but that’s not the same thing as inventing new words. Languages have patterns and rules, which is why even the simple set of digrams that Elite uses:

char pairs0[]= "ABOUSEITILETSTONLONUTHNOALLEXEGEZACEBISOUSESARMAINDIREA.ERATENBERALAVETIEDORQUANTEISRION";

char pairs[] = "..LEXEGEZACEBISO"               "USESARMAINDIREA."               "ERATENBERALAVETI"               "EDORQUANTEISRION"; /* Dots should be nullprint characters */

...gets better results than just throwing random letters together.

If you’re trying to create a new language (and there are many constructed language enthusiasts) then you’ll probably want something that takes into account the deeper patterns in the language, like syllable types, non-English-vowels, sound changes, and so on.

There’s several generators for conlang creators to use as inspiration for new words that fit the patterns of their languages, but I’m just going to talk about one today: Mark Rosenfelder’s Gen, which goes together with his guide for creating constructed languages.

The settings for Gen’s generator reflect the language that it’s generating for. If a language (like Quechua) only allows syllables with a optional-constant-required-vowel-optional-constant pattern, the generator need to take that into account. Likewise, a language can have different sets of vowels: Cusco Quechua only has /a/, /i/, and /u/, while Danish has /ɑ a æ ɛ e i o ɔ u ø œ ɶ y ʌ ɒ/. This obviously affects the kinds of words that fit.

The Sound Change Applier is even more about the hidden processes of language, in this case the way that pronunciation tends to shift over time.

Once again, the hidden structure is an important source of order for the generator. This is a principle that can be applied to more than just language generation: what structure is important for the thing you’re trying to generate? Find that and you’ll find what’s important about the thing you’re generating.

That’s also why generators can be an effective form of rhetoric: by describing a process, it can also act as a critique of the system that process creates.

so i saw those letter distribution graphs that are going around, and decided it would be fun to use that in a little word-generation program?

so i wrote up a quick and dirty little script to randomly generate words from slightly weighted arrays of consonants and vowels, based on the position in the word.

it’s super simple, and the pattern for word generation is kinda arbitrary, but whatever.

so i decided it’d be fun to fix it up a bit for use as a sample/nonsense/filler text generator, for layout purposes or whatever?

so it can now spit out paragraphs like this:

Wa bevaye tineki we Haf cicnizek wajhohure. Hafgosefyd Pi tefgofyme hinile wa walarade bo. Femymy wosere wipero wole baf bazimizefe biyec wanitonede fahakeke mimizotede. He citnikase bonise gaz bisyzem tivoyeye bekumi wenaninyye. Tiw hekome talohane babori co wafgikidyle ba ceruburen Tahiny. Bapaseyy wame he gi fagyde we witese taw wiyeyet wezile. Casyly ba fa bereser cahu jepec menotedef. Gasese ce co baniyycec misere Ma hafhovagy gopryde. Cote ganipi Hatker cakuni febu ta haw wemsede watmefere cinades. Jezova hibzyze hahobug taho po bavise ba teriluser Heseye bi. Wafek covazunefy gany toc wanaku garuhafety hi. Bafe payem bese getlafyme wa wabosene. Fe fa wamode tocfgili taheseky tiprade tarubo. Fe fa gepirore Gare cele weceset bow mefginary babbuholeye. Ha tibi ho Wewrihuye pa gase bittasykys tofnaruse wazaho to.

"i have that dreadful damning arraignment and listening like in a high"

"soundingswith the soul of science sake of the of strange story invisible to the men ive got sight for"

Some of the markov chain "sentences." I'll probably move on from this model very soon for generating sentences if I keep doing it. Proper punctuation would be cool to acheive, but I need my model to provide more context to each word selection.

I knew about markov chains before starting this, but for future stuff I want to see what I come up with before looking at common algorithms.