GiellaLT Documentation

GiellaLT provides rule-based language technology aimed at minority and indigenous languages

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Suggestion Weighting

The files for weighting, and thus decide the priority of suggestions, on our computer spellcheckers are found in


Levenshtein transitions and adjustments

This is the basis against which the operations we specify will be compared against.

Each Levenshtein operation is 10 points (this value is system-specific, it is set in the hfst/ The makefile also specifies the number of edit operations allowed. Standard value is 2.

Error model A: Adjusting Levenshtein

Levenshtein may be adjusted in two ways. The adjustments are single letters or

At the moment, what weight to put to any given pair is open. As for one rule of thumb, a multiply-occuring error (say an a/á pair, which may occur more than once in a given word (say, when writing *arrát for correct árrat should give both pairs a:á and á:a weights less than half of one Levenshtein operation, in order to outperform competing pairs involving one Levenshtein operation.

Single letters

The file is hfst/editdist.default.txt.

In the beginning of the file, all letters that participate in the suggestion fst are added.

In the suggestion there is a mapping from each letter to each other letter. The weight for all these transitions is in our case 10.

When the transition pair operation is listed in editdist.default.txt, it will get the weight listed instead of 10 points.

ç	č	-9
a	á	-6
á	a	-6
a	â	-6


The file is hfst/strings.default.txt.

The format is

c:cc	-2
cc:c	-2
d:dd	-2
g:gg	-2

This weight also come instead of the basic Levenshtein form.

String pairs are used as follows:

We build a Levenshstein 1 model, i.e. a set of all word pairs consisting of input : Lev1. Then, these two compete against each other, and any member of the stringpair with weight under Levenshtein 1 will win.


The file is hfst/final_strings.default.txt.

The format is

esnie:esne	-5
ese:asse	-5
htasse:htse	-5

These weights come in addition to the aggregated Error model A, the values are added to the number. Error model A may thus be run a number of times (standard = 2, as we saw), and the final_strings value is added after that.

initial letter

The file is hfst/initial_letters.default.txt

The format is

l:l	0.0
m:m	0.0
n:n	0.0
o:o	0.0

Using this may give a very large error model, and it is thus turned off as default.

Error model B: Swapping words

The file is words.default.txt.

The format is:

jih:jïh	0.0

These full word pairs will get a weight. This weight should be put lower than may be achieved from Error model A, in order to always outperform it (hence with negative weight in case Error model A operates with negative weights). Thus set up, one may even consider running Error model B as autocorrect (without asking for user confirmation).

Frequency and tag weight

Frequency weighting

The file is spellercorpus.raw.txt. (evt. a .clean. file)

A corpus may be used as a frequency weighting mechanism. Give the system a correctly spelled corpus. You then give least weight to the most frequent wordforms, according to a logarithmic scale (source: Tommi Pirinen). Thus, more common words get lower weights (inverse sfrequency values).

You may even take a specialised speller for learners, tuning frequency for a (corrected) learner’s corpus.

Tag weighting

The file is weighting/tags.reweight.

File format:

+Pot    +1
+Cond   +1
+Actio  -1
+Ess    +1
+Par    +1
+PxSg1  +3

Interaction between frequency and tag weighting

Logarithmic frequency values and tag weights are added together to get the aggregated grammatical/frequence weight.

Putting it all together

Text frequency and tag weight come on top of the error model. Typically, they will only come in when the error model gives (almost) a tie. Frequency should thus be tuned so that it does not override the error models, but rather gives priority within each (small amount of) edit distance step(s).


At the end of the day, tuning edit distance, letter and string pairs, against word frequency and each other is a linguistic and empirical question.

In order to find the ideal balance, a speller testbench is needed.