Agglutinative Language

The smallest unit in language that has meaning is called a morpheme. Some languages use relatively few morphemes-per-word like English: for example, the word "cats" can be broken into two morphemes -- "cat" and "-s". "Two" can't be broken down any further, so it has a 1-to-1 mapping between morphemes and words.

Other languages use a lot of morphemes-per-word. One strategy to create words from morphemes is called agglutination (meaning to glue things together). An agglutinative language takes all the morphemes that are going to go into a word, and with minimal or no changes, glues them together to form a word.

For example, the Yupik word "tuntussuqatarniksaitengqiggtuq" means "He had not yet said again that he was going to hunt reindeer". It is formed by taking the following morphemes and agglutinating them:

"tuntu-ssur-qatar-ni-ksaite-ngqiggte-uq"

I often wonder how much of a head start the isolating nature of English gave for computing. It allowed ignoring a lot of inflectional and agglutinative complexity.

Concretely I mean it's very easy to generate text using sentence templates. Just plug in words and it works out. "The $process_name has completed running." "Like $username's comment" "Ban $username".

Relatedly, I think focusing NLP efforts on English masks a lot of interesting phenomena, because English text already comes in a reasonably tokenized, chunked up and pre-digested, easy to handle form. For example speech recognition systems started out with closed vocabularies, with larger and larger numbers of words, and even in their toy forms you could recognize some proper English sentences. To do that in Hungarian for example, the "upfront costs" to a "somewhat usable" system are much higher, because closed vocabulary doesn't get you anywhere. (Similarly, learning basic English is very easy, you can build 100% correct sentences on day 1, you learn "I", "you", "see" and "hear" and can say "I see" and "You see" and "I see you" and "I hear Peter" which are all 100% correct. In Hungarian these are "nézek", "nézel", "nézlek", "hallom Pétert" requiring learning several suffixes and vowel harmony and definite/indefinite conjugation. The learning curve till your first 100% correct 3-5 word sentences is just steeper.)

I don't mean it's impossible to handle agglutinative languages in NLP, I just mean the "minimum viable model" is much simpler and attainable for English, which on the one hand was able to kickstart and propel the early research phases and on the other hand perhaps fueled a bit too much optimism.

English can seem very well structured and it can tempt one to think of language in a very symbolic, within-the-box, rule-based way. In terms of syntax trees, sets of valid sentences etc, instead of "fuzzy probabilistic mess" that it really is. Surely, the syntax tree, generative grammar approach (Chomsky and others) gave us a lot of computer science, but this kind of "clean" and pure symbolic parsing doesn't seem to drive today's NLP progress.

In summary, I wonder how linguistics and especially computational linguistics and NLP would have evolved in a non-Anglo culture, e.g. Slavic or Hungarian.

The i is due to using English keyboard, it's a bit tricky to type the proper i without autocompletion.

Being able to encode it reasonably in 5 bits and comfortably in 6 (adding case and a few last nice symbols) was helpful too.

Seems like you're talking about autocompletion, not autocorrect. In autocorrect you have completed the word, hit space and then the software fixes your typos. In autocomplete you get a list of suggested words while typing and you can tap them if your intended word is shown.