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FST Morphology for the Endangered Skolt Sami Language
Jack Rueter, Mika Hämäläinen
Department of Digital Humanities
University of Helsinki
{jack.rueter, mika.hamalainen}@helsinki.fi
Abstract
We present advances in the development of a FST-based morphological analyzer and generator for Skolt Sami. Like other minority
Uralic languages, Skolt Sami exhibits a rich morphology, on the one hand, and there is little golden standard material for it, on the other.
This makes NLP approaches for its study difficult without a solid morphological analysis. The language is severely endangered and the
work presented in this paper forms a part of a greater whole in its revitalization efforts. Furthermore, we intersperse our description with
facilitation and description practices not well documented in the infrastructure. Currently, the analyzer covers over 30,000 Skolt Sami
words in 148 inflectional paradigms and over 12 derivational forms.
Keywords: Skolt Sami, endangered languages, morphology
1. Introduction
Skolt Sami is a minority language belonging to Sami
branch of the Uralic language family. With its native speak-
ers at only around 300, it is considered a severely endan-
gered language (Moseley, 2010), which, despite its pluri-
centric potential, is decidedly focusing on one mutual lan-
gauge (Rueter and Hämäläinen, 2019). In this paper, we
present our open-source FST morphology for the language,
which is a part of the wider context of its on-going revital-
ization efforts.
The intricacies of Skolt Sami morphology include qual-
ity and quantity variation in the word stem as well as
suprasegmental palatalization before subsequent affixes.
Like Northern Sami and Estonian, Skolt Sami has conso-
nant quantity and quality variation that surpasses that of
Finnish, i.e. Skolt Sami has as many as three lengths in
the vowel and consonant quantities in a given word.
The finite-state description of Skolt Sami involves develop-
ing strategies for reusability of open-source documentation
in other minority languages. In other words, the FST de-
scription is designed in such a fashion that it can be ap-
plied to other languages as well with minimal modifica-
tions. Skolt Sami, like many other minority Uralic lan-
guages, attests to a fair degree of regular morphology, i.e.,
its nouns are marked for the categories of number, pos-
session and numerous case forms with regular diminutive
derivation, and its verbs are conjugated for tense, mood
and person in addition to undergoing several regular deriva-
tions. Morphological descriptions have been developed in
the GiellaLT (Sami Language technology) infrastructure at
the Norwegian Arctic University in Tromso, using Helsinki
Finite-State Technology (HFST) (Lindén et al., 2013).
Working in the GiellaLT infrastructure, it is possible to ap-
ply ready-made solutions to multiple language learning, fa-
cilitation and empowerment tasks. Leading into the digital
age, there are ongoing implementations, such as keyboards1
for various platforms, and corpora2, being expanded to
provide developers, researchers and language community
1http://divvun.no/keyboards/index.html/
2http://gtweb.uit.no/korp/
members access to language materials directly. The trick is
to find new uses and reuses for data sets and technologies
as well as to bring development closer to the language com-
munity. If development follows the North Sámi lead, any
project can reap from the work already done.
Extensive work has already been done on data and tool
development in the GiellaLT infrastructure (Moshagen et
al., 2013) and (Moshagen et al., 2014), and previous work
also exists for Skolt Sami3(Sammallahti and Mosnikoff,
1991; Sammallahti, 2015; Feist, 2015). There are online
and click-in-text dictionaries (Rueter, 2017), 4spell check-
ers (Morottaja et al., 2018), 5, these are implemented in
OpenOffice, but some of the more prominent languages
are supported in MS Word, as well as rule-based language
learning (Antonsen et al., 2013; Uibo et al., 2015). For
languages with extensive description and documentation,
there are syntax checkers (Wiechetek et al., 2019), machine
translation (Antonsen et al., 2017) and speech synthesis and
recognition (Hjortnaes et al., 2020), just to mention the tip
of the iceberg (Rueter, 2014). From a language learner
and research point of departure, the development and ap-
plication of these tools points to well-organized morpho-
syntactic and lexical descriptions of the language in focus.
By well-organized descriptions, we mean approaching
tasks at hand with applied reusability. Reusability is illus-
trated in the construction of a morphological analyzer for
linguists, which, due to the fact that it is able to recognize
and analyze regular morphological forms, can also serve as
a morphological spell checker. In fact, this same analyzer
can be reversed and used as a generator, which is useful
in providing language learners with fixed, analogous and
random tasks in morphology. The same morphological an-
3http://oahpa.no/sms/useoahpa/background.
eng.html/, read further in this article for subsequent develop-
ments in http://oahpa.no/nuorti/
4The forerunner https://sanit.oahpa.no/read/, an
online dictionary here, and on analogous pages of other dic-
tionaries, (e.g., https://saan.oahpa.no/read/), can be
dragged to the tool bar of Firefox and Google Chrome
5http://divvun.no/korrektur/korrektur.
html/
arXiv:2004.04803v1 [cs.CL] 9 Apr 2020
alyzer, when augmented by glosses, can immediately begin
to provide online dictionary and click-in-text analyses.
The development of an optimal morphological analyzer and
glossing for a language like Skolt Sami requires concise
morphological and lexical work, on the one hand, and ac-
cess to corpora including language learning materials, on
the other. Corpora provide access to language in use, and
language learning materials help to establish a received un-
derstanding of the language. To this end, the morphologi-
cal analyzer for Skolt Sami has been constructed to analyze
and generate a pedagogically enhanced orthography, for in-
dication of short and long diphthongs preceding geminates
as well as mid low front vowels, as might be rendered in a
pronouncing dictionary. One such example might be seen
in the word kue
˙
0tt ‘hut’ as opposed to the literal norm kue0tt,
where the dot below the enot only indicates a slightly low-
ered pronunciation of the vowel but also assists in identi-
fying the paradigm type, kue
˙
0tt :kue0ąid ‘hut+N+Pl+Acc’
versus kue0ll :kuõ0lid ‘fish+N+Pl+Acc’.
By focusing on the construction of a pedagogical enhanced
analyzer-generator, teaching resources can be developed
that target randomly generated morphological tasks for the
language learner as in the North Sami learning tool Davvi 6.
In any given language reader, there are texts with words in
various forms and an accompanying vocabulary. While vo-
cabulary translation can readily be utilized as a fixed task in
language learning, inflectional tasks, especially in morpho-
logically rich languages, can be developed as random exer-
cises. Although the contextual word forms in the reader are
quite limited, it is possible to construct randomized mor-
phological exercises where the student is expected to in-
flect nouns, adjectives and verbs alike in forms that have
been taught but not explicitly given for the random words
provided in the reader vocabulary, e.g. in nouns the student
may select vocabulary from reader Achapters 1–5 with a
randomized task for nouns, plural, comitative, third person
singular possessive suffix: +N+Pl+Com+PxSg3. Essen-
tially all nouns in the selected vocabulary available for this
reading are inadvertently presented to the learner.
2. Related Work
In the past, multiple methods have been proposed for auto-
matically learning morphology for a given language. One
of these is Morfessor (Creutz and Lagus, 2007), which is a
set of tools designed to learn morphology from raw textual
data. It has been developed with Finnish in mind, and this
means that it is intended to perform well with extensive reg-
ular morphology, i.e. morphologically rich languages, too.
Bergmanis and Goldwater (Bergmanis and Goldwater,
2017) present another statistical approach that can also take
spelling variation into account. Their approach is based on
the notion of a morphological chain consisting of child-
parent pairs. When analyzing the morphology of a lan-
guage, the approach takes several features into account such
as presence of the parent in the training data, semantic sim-
ilarity, likely affixes and so on.
Such statistical approaches, however, are data-hungry. This
is a problem for various reasons in the case of Skolt Sami.
6http://oanpa.no/davvi/morfas/
The scarce quantity of textual data is one limitation, but it
is even a greater one given that the language is still being
standardized and the users provide a variety of forms and
vocabulary when expressing themselves in their native lan-
guage. This means an even greater variety in morphology
that the statistical model should be able capture from a lim-
ited dataset.
In the absence of a reasonably sized descriptive corpus of
the language, annotated or not, the most accurate way to
model the morphology is by using a rule-based methodol-
ogy.
FSTs (Finite-State Transducers) have been shown in the
past to be an effective way to model the morphology even
for languages with an abundance of morphological features
(cf. (Beesley and Karttunen, 2003)). Perhaps one of the
largest-scale FSTs to model the morphology of a language
is the one developed for Finnish (Pirinen et al., 2017). This
tool, Omorfi, serves as the state-of-the-art morphological
analyzer for Finnish.
3. The FST Model Development Pipeline
Developing a morphological description of a language pre-
supposes a language-learning and documentary approach.
Other people have learned the language and become profi-
cient in it before you, so extract paradigms from grammars,
readers and research to build the language model. If you
are the first researcher to describe the language, take hints
from the language learners, if there are any, they may be
still developing their own understanding of the language
morpho-syntax, and, at times, they may provide you with
informative interpretations of the language.
Idiosyncrasies of a language can, sometimes, be captured
through comparison to those of another. When a descrip-
tion of Skolt Sami, Finnish, Estonian, etc. introduces alien
phenomena, such as word-stem quality and quantity vari-
ation as well as suprasegmental palatalization, it is a good
idea to try describing them both separately and in tandem.
Word-stem quality variation affects both consonants and
vowel. In consonants, an analogous English example might
be illustrated with the f:vvariation found in the English
words life,lives and loaf,loaves. From a historical perspec-
tive, the verb to live will serve as an instance where long
and short vowels accentuate a distinction between nouns
and verbs. In a like manner, the English verb paradigm
(sing,sang,sung) provides a sample of vowel variation
with regular semantic alignment in other verbs, such as
swim and drink. These seemingly peripheral phenomena
of English, however, are central to the description of Skolt
Sami morphology, where consonant quality and quantity
variation permeate the verbal and nominal inflection sys-
tems. Suprasegmental palatalization is yet another phe-
nomenon to be dealt with, as it may present its own influ-
ence on sound variations in both the consonants and vowels
in the same coda of a word stem. These require sound vari-
ation modeling in what is referred to as a two-level model,
where awareness of underlying hypothetical sound patterns
and surface-level reflexes are united to facilitate analysis
and generation of paradigmatic stem type variation, e.g.
an underlying sw{iau}m could be configured with a ˆ VowI
trigger to call the form swim,ˆ VowA the form swam, and
ˆ VowU the form swum.
Theoretically speaking, Skolt Sámi has vowel and conso-
nant quantity variation in three lengths, i.e. monophthongs
and diphthongs as well as geminates and consonant clus-
ters are subject to three lengths. One problem with the ini-
tial finite-state description of Skolt Sami was that attempts
were made to describe Skolt Sami according to the comple-
mentary distibution of quantity found in North Sámi7.
By chance, the author set out to describe vowel and conso-
nant quantity as separate conjoined phenomena, and when
the instance of short vowel and shortened consonant in tan-
dem presented itself, only a little extra implementation was
required for identifying this new variation. In fact, the phe-
nomenon had been described earlier as allegro versus largo,
but it had been ignored in some of the linguistic literature
(Koponen and Rueter, 2016).
Preparing the description of a single word is much like writ-
ing a terse dictionary entry. The required information con-
sists of a head word form or lemma, a stem form from
which to derive all required stems, a continuation lexicon
indicating paradigm type (part of speech is also interest-
ing), and finally a gloss or note. The word radio ‘radio’
might be presented as follows:
radio+N:radio N_RADIO ''radio'' ;
The LE MMA:STE M CONTINUATION-LEXICON NOTE pre-
sentation represents one line of code consisting of four
pieces of data. First, comes the index, which consists of the
lemma and part-of-speech tag. Second, after a separating
colon, comes the stem, which, with the Continuation lexi-
con (third constituent) make paradigm compilation possible
by indicating what base all subsequent concatenated mor-
phology connects to – the loanword ‘radio’ has no stem-
internal variation. Finally, there is the optional NOTE con-
stituent, where a gloss has been provided.
The Continuation lexicon name has been written in upper-
case letters to distinguish it from the remainder of the code
line. In this language, continuation lexicon names are ini-
tially marked for part of speech, hence the initial ‘N_’. This
part-of-speech increment is more of a mnemonic note to
help facilitate faster manual coding. After initial denom-
inal derivation lexica, nouns, adjectives and numerals are
directed to mutual handling of case, number and possessive
marking.
This initial line of code may encode even more complex
data. One such entry might be observed in the noun ve0rdd
‘stream’, which exhibits necessary information for complex
stem variation:
ve0rdd+N:ve
˙
ˆ1VOW{0Ø}rdd N_KAQLBB ''flow,
stream'';
The index ve0rdd+N: (LEM MA constituent and part-of-
speech tag), as such, is readily comprehensible. The part-
of-speech tag may also be preceded by tags indicating vari-
ants in order of preference (+v1,+v2) and homonymity
7In North Sámi, there is a three-way gradation system where
grade one has an extra-long vowel and short consonant, grade two
has a long vowel with a long consonant, and grade three has a
short vowel with an extra-long consonant.
(+Hom1,+Hom2), and it may be followed by tags indicat-
ing semantics (+Sem...) and part-of-speech subtypes (e.g.
+Prop for proper nouns, +Dem as in demonstrative pro-
noun). Tags, of course, may be inserted at the root or in
subsequent continuation lexica – this is simply a matter of
taste and the complexity of the continuation lexicon net-
work.
The ST EM ve
˙
ˆ1VOW{0Ø}rdd in combination with the
CONTINUATION-LEXICON N_KAQLBB is what captures
the proliferation of six separate stem forms used in regu-
lar inflection: ve0rdd ‘S G+NOM’, vee0rd ‘SG+GEN’, ve
˙rdda
‘SG +ILL’, vii0rdi ‘PL+G EN’, ve0rdstes ‘S G+LOC+PXSG 3’,
ve
˙e
˙rdaž ‘DIMIN+SG +NOM’. While vowel and consonant
variation might be considered peripheral in English, these
extensive patterns are wide-spread in Skolt Sami inflection.
Some verb types may even have as many as eleven sepa-
rate stem forms used in regular inflection and derivation.
Hence, consonant and vowel quality together with quantity
in both provides a challenge for description of the regular
inflectional paradigms of Skolt Sámi.
The continuation lexicon N_KAQLBB mnemonically points
to the Skolt Sámi word kä0lbb ‘calf (anim.)’ as a reference
to paradigm type.
Reference to paradigms has traditionally been done using
numbers. This entails access to a set of paradigm descrip-
tions, because no one can be expected to memorize large
sets of paradigm types by number alone. Using familiar
words to allude to paradigm types, however, may be straight
forward from a native speaker’s perspective, but they too
will require documentation in test code. Test codes might
be located adjacent to the appropriate affix continuation
lexicon or in a separate set of test files (see also the noun
algg ‘beginning’ in Figure 1, below). The NOTE section, of
course, is open for virtually any type of data.
Development of guidelines helps newcomers join a tra-
dition and construct analogous, parallel descriptions in
the same or similar infrastructures. The presupposition
of a willingness to adapt new projects to the practices
of established analogous work is an important element in
open-source FST development at GiellaLT, which has been
adopted as the basis for guideline development. At Giel-
laLT documentation is sometimes sparse, incomplete or dif-
ficult to find, and therefore it is imperative that all possi-
ble reference be made to shared practices. For maximal-
ized short term achievement (2 to 5 years), the project lan-
guages to consult first are North Sami (sme) and South
Sami (sma), whereas the experience from the Skolt Sami
language project is discussed here.
Skolt Sami specific descriptive materials have been dealt
with in the light of work in closely related languages. Here,
practice with analogous work in other Sami and Uralic lan-
guages has been helpful in learning mnemonic methods that
can be applied as well as lexicon code line writing and
sound variation modeling. Each language has many of its
own requirements, but, where ever possible, we should seek
out ways to align all projects.
The tag sets used with various language parsers at GiellaLT
are extensive and have been directly adapted to work in the
Skolt Sami project to ensure a high usability of tools al-
ready implemented and in mutual use in many language
projects. Ordering of tags reflects parsing no later than
2005, e.g. N+Sg+Nom giehta ... (Sjur Moshagen and
Trosterud, 2005). Inflection types are indicated mnemon-
ically by use of a frequent representative of the type, a
strategy also observed in Omorfi, e.g. an initial continu-
ation class marking N_ALGG (algg ‘beginning’) is given
for nouns with a coda structure in VhighC1C2C2. Inflection
type naming of this kind draws the developer’s attention to
the familiar word and helps to minimize specification con-
sultation required when inflection types are only numeri-
cally coded, e.g. 1, 2, 3... Both systems, however, require
set specifications for each inflection type.
In order to enable morpho-lexical variation detection, FST
description presupposes a degree of wrong form genera-
tion. Indeed, wrong form coverage is what facilitates in-
telligent spell checking suggestions, e.g. generation of a
four-year-old’s simple past rendition, swimmed, with a hint
tag +regular-past-error could be useful. For extended cov-
erage, more inflection types and extensions are described
than would otherwise be assumed from mere phonologi-
cal descriptions. There is diversity in the spoken language,
which has meant that certain stem types or individual forms
must be provided with multiple realizations. Here we want
to avoid assigning multiple paradigms to individual lem-
mas where the distinction between the paradigms may lie
in only one or two forms (cf. (Iva, 2007)).
In Skolt Sami building a slightly more demanding descrip-
tion of the phonology has meant the inclusion of otherwise
pedagogical characters and graphemes. Special filtering is
available for converting pedagogic target transducers into
normative transducers and spell relaxes extend these in turn
to descriptive transducers. These same methods are shared
by other language projects in the GiellaLT infrastructure. In
the long run, tweeking the description for pedagogic target-
ing means that even more uses are being made available,
and that basic work is almost immediately available for
continuation projects already realized or under construction
in other language projects, i.e. syntactic disambiguation,
text-to-speech, etymology suggestion.
3.1. Development of the two-level description
Skolt Sami Finite-state transducer development reuses de-
scriptive materials for both concatenation strategies and
testing. Work in the GiellaLT infrastructure begins with
generation-analysis code test files (yaml), with content as
in (Figure 1). Each line contains a lemma, subsequent tag
set and resulting output word form or forms following a
colon, e.g. algg+N+Sg+Gen: aalg.
The lines of description in the yaml test file (lemma + tag
set + resulting word forms) are readily copied to a lexc af-
fix description file for further editing and implementation
as code (Figure 2). Here it can be observed that concate-
national morphology is added after the :colon, but at the
same time there is a certain amount of further required mor-
phological quality and quantity change.
Editing in the continuation lexica in the affixes/*.lexc files
entails stripping the lemma and the part of the target word
forms that can serve as the stem. Since Skolt Sami is not
a language with entirely simple concatenation strategies,
we can make a few observations of the interplay between
Figure 1: A diagram showing file content for yaml
analyzer-generator testing
Figure 2: A diagram showing LEXICON development for
ALGG type nouns
simple morphological concatenation and the complemen-
tary two-level model facilitation.
The lemma for the word algg ‘beginning’ is the same as
the nominative singular and has no morpho-phonological
changes, hence no triggers are present when coding
+N+Sg+Nom. In the genitive and accusative singular,
however, coding +N+Sg+Acc co-occurs with coda vowel
lengthening indicated with the trigger V2VV (lengthening,
i.e. one vowel becomes two) and consonant cluster weaken-
ing indicated with the trigger XYY2XY (i.e. the consonant
cluster altenation in -lgg and -lg) (compare concatenation
and phenomena in Figure 2), on the one hand, and the com-
pound of concatenational morphology with accompanying
triggers V2VV and XYY2XY, on the other in (Figure 3).
Figure 3: A diagram showing some triggers used in de-
scription of ALGG type nouns
The .yaml code test content can be further utilized as
in-line testing code by simply flipping content left-to-
right for analysis reading, as shown in (Figure 4). Im-
plicit in the test data, we can observe five different
stems for the monophthong noun algg:algg ‘Sg+Nom’,
aalg‘Sg+Gen’, a0lˇ
gˇ
ge ‘Sg+Ill’, algstan ‘Sg+Loc+PxSg1’,
aa0lje ‘Dimin+N+Sg+Gen’.
Figure 4: A diagram showing some test data for ALGG
type noun analysis
Although there are instances of single stems taking nu-
merous affixes, e.g. biografia or radio, above, most
nominals and verbs require multiple stems. The exten-
sive stem variation observed in the noun algg, above,
is surpassed in the verb tie0tted ‘to know’. It uses the
following 10 stems in regular inflection: tie0tt- ‘Inf’,
tie0ą-‘Ind+Prt+Sg3’, tiõt’t- ‘Imprt+ConNeg’, tiõą-‘Deriv’,
tiõ0t’t- ‘Ind+Prt+Pl3’, tiõ0ą-‘Pot’, teât’t- ‘Imprt+Pl3’, teâtt-
‘Ind+Prs+Sg3’, teâą-‘Cond’, teä0t’t- ‘Ind+Prs+Pl3’. The
vowel quality variation in Skolt Sami and North Sami is
analogous to what is observed in Germanic irregular verbs,
e.g. sing,sang,sung.
Skolt Sami provides a challenge deserving of morpholex-
ical and two-level model descriptions as introduced origi-
nally (Koskenniemi, 1983) integration. Integration of con-
catenation lexicon and morphophonological two-level de-
scription has required both intuition and a working knowl-
edge of the target language. Whereas concatenation al-
ludes to simply adding one morpheme to another, morpho-
phonology draws our attention to changes required in the
stems; hence the challenge of defining 10 separate stems
for a single lemma in Skolt Sami provided above. (More ex-
tensive descriptions of quality, quantity and suprasegmental
variation are provided in (Feist, 2015; Sammallahti, 2015).)
The two-level model utilizes parallel constraints for phono-
logical description. As mentioned above, descriptive gram-
mars of the Skolt Sami language indicate multiple simul-
taneous, coordinated variation in the stem. Thus work on
the two-level model initially opted to provide separate trig-
gers for each individual phenomenon, here ˆ V2VV quantity,
ˆ VowRaise quality and ˆ PAL palatalization.
In brief, triggers are an artificial means of replacing the
natural phonological features occurring in the morphology.
They can be used for causing phenomena subsequent (right-
context here) or preceding (left-context). For example, if
front-back vowel harmony is highly predictable on the basis
of the preceding stem, the individual stems can be marked
{front} or {back} triggers in order to elicit the front or
back allomorphs of subsequent suffixes, i.e. triggers are set
for right-context phenomena. A trigger provides for ma-
nipulation of the harmony reflexes necessary for incorrect
morphology, as well, i.e. something needed in recogniz-
ing misspellings in intelligent computer-assisted language
learning and spell checker suggestions – let us remember
the instance of swimmed, above.
The two-level model rules facilitate simultaneous variation
of many features in the same word. Left and right con-
texts play an important role in this description, whereas
both contexts can contain morpho-phonological phenom-
ena seen to precede or follow the change elicited by a given
rule, or they can disregard them. Triggers are used in rule
writing, because the actual morphophonology of the words
does not necessarily reflect idealconsistant trigger pattern-
ing.
Zero-to-surface-entity rules present in the early phases of
the project have been corrected by adding multicharacter
archiphones to the individual stems. Stem-internal change
such as matters of vowel quantity and quality are indicated
with these symbols. For purposes of phenomenon recog-
nition, curly brackets have been used for displaying arrays
of variation, e.g. {eöâä} indicates there is a vowel vari-
ation of four separate qualities as required in the various
stems. Parallel multiple-character symbols have been im-
plemented for suprasegmentals, length markers, etc. Stem
variation in the word.
Modeling quantity in Skolt Sami has meant a divorce from
the description of other Sami languages. Quantity varia-
tion is generally viewed as a coordinated phenomenon af-
fecting vowel and consonant length simultaneously (see
reference to North Sámi and complementary distribution
of quantity, above). Skolt Sami deviates here: The pre-
dictable ‘extra long vowel + short consonant’; ‘long vowel
+ long consonant’, ‘short vowel + extra long consonant’
combinations are supplemented by a fourth ‘extra short
vowel + extra short consonant’ pattern. The four-way
split required little new coding; original quantity model-
ing had treated vowel and consonant length as separate
phenomena. When the fourth pattern became more ap-
parent after the first half year, all triggers were present,
and actually little work was required to implement their
use. Since the fourth pattern alternates with the long-
vowel-long-consonant pattern algstan (allegro) ∼aalgstan
(largo), respectively ‘begin+N+Sg+Loc+PxSg1’, more lan-
guage documentation was required, as this variation was
found to permeate the inflection and derivation pattern of
the language.
Modeling quality in Skolt Sami has introduced multi-
character symbols in the stem. These multi-character sym-
bols contain arrays of realizations in commented curly
brackets, e.g. t%{ie%}%{eöâä%}%{0Ø%}tt ‘to know’,
above. Each array indicates a mnemonic list of variables.
These lists are easy to interpret and consistent with guesser
and cognate search development, where sound change is
consistently traceable (Kimmo Koskenniemi and Heiki-
Jaan Kaalep, pc.). Moreover, array notations are analo-
gous with inflection group identifying model words as in
N_ALGG and N_KAQLBB, above.
Variation in the multi-character symbols as well as the
unmarked consonants is modeled with triggers. Triggers
are used to elicit vowel length and height, suprasegmental
palatalization (which may affect the realization of both the
preceding vowel and subsequent consonantism), as well as
consonant length and quality. In the Skolt Sami project,
vowel length is triggered with the multi-character symbols
%ˆ V2VV (short to long) and %ˆ VV2V (long to short).
To avoid balancing problems introduced with flag diacritics
and further unexpected complications, triggers are ordered
and follow the stem before concatenated suffixes. The tie0ą-
stem required for rendering the form V+Pot+Sg3: tie0ąež
is elicited with the consecutive triggers: %ˆ VOWRaise,
%ˆ PALE, %ˆ PAL and %ˆ CC2C, i.e. vowel raising (which
would regularly render iõ), suprasegmental coloring (ren-
dering iõ ⇒ie), palatalization ( 0) and consonant quality
change via shortening. The large number of triggers de-
manded a large memory, and to alleviate the problem a
reversed-intersect function was implemented in the Giel-
laLT infrastructure as recommended by a member of the
HFST team.
3.2. Deviation from Point of Departure on
GiellaLT
The Skolt Sami project has seen departure from previous
work in the infrastructure but simultaneously adherence to
a mnemonic system of description. In the course of the
project, the policy of lemma followed by a simple ortho-
graphic stem has not been retained. The number of nominal
stem types has risen to 308 from the 56 described in (Sam-
mallahti and Mosnikoff, 1991), while the number of verbal
stem types is 115 as compared to 30 (ibidem.). Adjectives
and numerals share inflection types with nouns. Before the
commence of the project in 2013, for instance, only 280
verbs and 828 nouns were partially facilitated by the sys-
tem, whereas by the end of 2018 the analogous figures were
4844 verb stems with over 40 conjugation forms as well as
numerous verbal and nominal derivations and 23683 noun
stems with over 98 declensional forms aa well as additional
derivations, and the entire lemma count exceeded 36000.
Multi-character symbol development endears mnemonic
forms. Arrays enclosed in curly brackets are used for in-
dicating vowel quality and quantity variation, a practice
analogous of inflection type model words that hint at the
type of stem variation. Triggers have, in matters of length,
been drafted to reflect specific nuances of coda description,
e.g. %ˆ VV2V indicates vowel shortening, %ˆ CCC2CC
geminate shortening, and %ˆ XYY2XY consonant cluster
shortening, respectively.
Triggers have been fashioned for and subsequent affixes.
The stem has been filled with multiple-character symbols to
indicate which letters and graphemes undergo change and
what kind of change. Ordered triggers have been applied
to bring about these changes regardless of the orthographic
context, which simplifies the generation of incorrect forms,
a necessity in the recognition of ill-formed word forms and
their alignment with the desired words.
Trigger ordering is aligned with the orthographic realiza-
Word Class glossed unglossed inflections derivations
Adjectives 4190 166 16 3
Nouns 21640 712 99 3+
Verbs 4845 23 33 6+
total 30675 901 148 12+
Figure 5: morpholexical coverage’
tion of phonological phenomena. Thus, changes in penulti-
mate syllables precede those in ultimate syllables, which is
similar to vowel changes preceding suprasegmental mark-
ing and subsequent consonants.A special context marker
Pen is used before each trigger effecting change in the
penultimate syllable. The trigger count in a given stem may
reach six.
4. Lexical and Morphological Coverage
In the absence of gold annotated data, we do not con-
duct an evaluation typical to the current mainstream NLP,
but rather describe the coverage of forms and lexemes in
the transducer. Here we will limit our discussion to the
most extensive paradigms, i.e. adjectives, nouns and verbs
(see Figure 5). In addition to statistics on glossed and un-
glossed lexicon, where glossed is a loose term for the pres-
ence of at least one single word translation for each Skolt
Sámi word in the Akusanat dictionary (Hämäläinen and
Rueter, 2018), we will discuss regular inflection and deriva-
tion. While inflection refers to conjugation and declension,
on the one hand, derivation indicates part-of-speech trans-
formation brought about by morphological means, on the
other. As a result of this work, the Skolt Sámi transducer
represents a lexicon of over 30,000 lemmas with a cover-
age of over 2.3 million inflectional forms, not to mention
the derivational exponent or compound nouns.
Adjectives in Skolt Sami may have special attribute forms
for use in the noun phrase, as is the situation in other Sami
languages. Adjectives are also known to decline in the same
case forms as nouns, which brings us to a total of approxi-
mately 16 paradigmatic forms associated with the declina-
tion of each adjective. Regular derivation, it will be noted,
is generally limited to comparative and superlative inflec-
tion will all cases as well as nominalization, which goes on
to feed regular noun inflection.
Nouns, like adjectives, can be declined in seven cases for
singular and plural with the addition of the partitive8. In
contrast to the adjectives, however, number and case can
be augmented with possession markers for three persons
and two numbers, which brings the number of paradigmatic
cells in declination to nearly 100. Nouns can further be de-
rived as regular diminutives (this again feeds regular deriva-
tion) and two types of adjectives with the meanings ‘with-
out X (privative)’ and ‘full of X’ (both of which can further
derived as nouns, and the former is regulary derived as a
verb).
The verbal paradigm is also relatively extensive. Each tense
and additional mood, with the exception of the imperative,
has three categories for person, two for number and an in-
definite personal form (7). Thus, in addition to two tenses
in the indicative, the subjunctive and potential mood there
8the partitive has no morphological distinction for number
are five more forms for the imperative, which brings us to
a total of 33 forms in a given conjugation paradigm. Non-
finite derivation, participles in addition to deverbal nouns
and verbs, adds feeders to nominal and verbal derivation
alike.
A large percentage of this regular inflection is in place and
available in the UralicNLP, a python library for Uralic mi-
nority languages (Hämäläinen, 2019). The lexical database
for Skolt Sami is also undergoing rigorous scrutiny and de-
velopment in the editing of the forth-coming Moshnikoff
Skolt Sami dictionary in Ve0rdd9, an open-source dictio-
nary environment for minority language community editor
and developer collaboration (Alnajjar et al., 2020). Ve0rdd
‘stream, flow’ also provides an interface for feedback into
the dictionary system.
5. Discussion and Future Work
The FSTs are released in GiellaLT infrastructure as a con-
stantly updating bleeding edge release. Efforts have been
made to bring the writing of the FST lexc materials into an
easier MediaWiki based framework (Rueter and Hämäläi-
nen, 2017). All edits to the FSTs made in the Medi-
aWiki platform are automatically synchronized with those
uploaded to GiellaLT.
According to statistics at GiellaLT for online dictionary us-
age, the Skolt Sami–Finnish dictionary enjoys a great pop-
ularity among the language community. It is only second
to North Sami–Norwegian (Trosterud, p.c. 2019–06–04).
Statistics provide pointers for where elaboration is needed
in definitions as well as the shortcomings of the transducer
(analysis of misspelled words).
In order to make the FSTs more accessible for other re-
sarchers conducting NLP tasks focused on Skolt Sami,
the FSTs have been made available through UralicNLP
(Hämäläinen, 2019). This is a specialized Python library
for NLP for Uralic languages which makes using FSTs
easier by providing a documented programmatic interface.
Furthermore, the library uses precompiled models, which
further facilitates the reuse of our FSTs.
Modeling diphthongs is still a challenge for Skolt Sami. Fu-
ture work will attempt to develop separate triggers for the
first and second element. Thus, the treatment of diphthongs
will be analogous to that of quantity. Especially front and
fronted diphthongs still offer unresolved variation in the
paradigms of a number of nouns.
FSTs provide a good starting point for development of
higher level NLP tools that embrace the new neural network
methods. For instance, FSTs can be used to generate paral-
lel sentences out of lexica and abstract syntax descriptions
to be used for neural machine translation in scenarios with-
out any real parallel data (Hämäläinen and Alnajjar, 2019).
Neural models for morphological tagging can as well ben-
efit from readings provided by FSTs (Ens et al., 2019).
6. Conclusions
We have presented the current state of our on-going project
of modeling Skolt Sami morphology. The transducers are
9https://akusanat.com/verdd/
made available in a continuously updated fashion in multi-
ple different channels, to promote their use in any tasks that
contributes to the revitalization of the language
The highly phonological Skolt Sami orthography has
strengthened the notion that one description might be uti-
lized in multiple tools, i.e. text-to-speech, orthographic,
pedagogical, etc. This has lead to the addition of two extra
characters in the alphabet and the addition of a pedagogic
dictionary type generator.
Mnemonic formation of inflection type indicators has
been followed by the formulation of mnemonic multiple-
character symbols and triggers. Triggers have been or-
dered, and regular inflection has been modeled to exceed
mere finite conjugation and nominal declension. Additional
trigger work may be required for the description of diph-
thong quality change and derivation, but this must be done
in collaboration with the language community, language re-
searchers and the normative body.
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