GiellaLT provides an infrastructure for rule-based language technology aimed at minority and indigenous languages, and streamlines building anything from keyboards to speech technology.

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Stavekontrolltesting og norplusprosjektet

2012-2013: LA-2012_1a-31112, Feilkorpus for å testa stavekontrollar for grønlandsk, islandsk, lulesamisk og nordsamisk

Språk & medarbeidarar

Language/role Project member
ISL Hulda Óladóttir
KAL Elin Neshamar
SME Thomas Omma
SMJ Inga Mikkelsen
Project lead & test bench development Sjur Moshagen

Maja Kappfjell has worked with SMA, but is formally not part of the project. We have tried to keep the SMA corpus in synch with the rest, though, in terms of markup standards and reaching a final state at the same time as the rest of the languages.

Basic facts

Project Target

Status as of now

Language No Typos No of running words % typos of all words
ISL 915 149 532 0,61 %
KAL 224 21 723 1,03 %
SMA 9074 41 682 21,77 %
SME 2370 47 293 5,01 %
SMJ 1170 22 536 5,19 %


There is a very big span in % of errors in the languages, from ISL 0,61% to SMA 21,77%. It is quite obvious, though, that in the SMA case, the main issue is one of norm:

The SMA norm prescribes the use of Swedish ö and Norwegian æ - a combination that is not easily available on any standard keyboard (unless you really search for it). This is causing most writers to just ignore this aspect of the norm, and they use either both Norwegian æ and ø all the time, or both Swedish ä and ö all the time. When the sounds these letters represent are also quite frequent, it leads to a situation where every sentence contains spelling errors.

Markup work

The texts

For all languages we tried to collect texts that would be representative of spelling errors made by native speakers - because that’s the main target user group for the spellers. At the same time the texts should be relavitely easy to collect - corpus text collection can be a very time consuming activity. What we ended up with was the following main text categories:

Most texts are available in an open-access corpus repository, but some of the texts are stored in a closed repository for copyright and privacy issues.

The testing done so far is only done on the freely available texts.






Test results

Precision & recall

ISL Precision: 9,5% Recall: 71,73% Accuracy: 95,7%
KAL Precision: 5,35% Recall: 82,59% Accuracy: 84,76%
SMA Precision: 85,64% Recall: 94,53% Accuracy: 95,36%
SME Precision: 72,88% Recall: 91,05% Accuracy: 97,85%
SMJ Precision: 54,57% Recall: 89,74% Accuracy: 95,59%

Speed & memory

Language Speed Memory use
ISL 37,31 words/second 17 172 Kb
KAL 990,2 words/second 58 952 Kb
SMA 24,11 words/second 4 480 Kb
SME 76,05 words/second 6 380 Kb
SMJ 112,03 words/second 5 104 Kb