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Show, Don' Tell : Visualising Finnish Wo d Fo ma ion in a B owse -Based Reading
Assis an
© 2020 ACL
Published e sion
Robe son, F ankie
Robe son, F. (2020). Show, Don' Tell : Visualising Finnish Wo d Fo ma ion in a B owse -Based
Reading Assis an . In D. Al e , E. Volodina, I. Pilan, H. Lange, & L. B. Bo in (Eds.), NLP4CALL
2020 : P oceedings o he 9 h Wo kshop on NLP o Compu e Assis ed Language Lea ning (pp.
37-45). LiU Elec onic P ess. Linköping elec onic con e ence p oceedings, 175.
h ps://www.aclweb.o g/an hology/2020.nlp4call-1.4
2020
Show, Don’ Tell: Visualising Finnish Wo d Fo ma ion in a
B owse -Based Reading Assis an
F ankie Robe son
Uni e si y o Jy ¨
askyl¨
a
[email p o ec ed]
Abs ac
This pape p esen s he NiinMik¨
aOli?! ead-
ing assis an o Finnish. The ocus is upon
he simpli ied p esen a ion and isualisa ion o
a wide ange o wo d-le el linguis ic phenom-
ena o he Finnish language in a uni ied o m
so as o bene i language lea ne s. The sys em
is a ailable as a b owse ex ension, in ended
o be used in-con ex , wi h au hen ic ex s, in
o de o encou age ee eading in language
lea ne s.
1 In oduc ion
This pape p esen s an in elligen eading assis an
o Finnish. The sys em, NiinMik¨
aOli?! (English:
TheWha Now?!), p esen s wo d and idiom de ini-
ions in-con ex . The sys em can be used h ough a
web in e ace ei he as a dic iona y o by manually
en e ing o copying ex in o a ex ield, o ide-
ally, as a b owse ex ension o assis wi h eading
Finnish web pages. When used as a b owse ex-
ension, NiinMik¨
aOli?! p esen s wo d de ini ions
in a sideba .
The e is inc easing in e es in con ex ualised
lea ning o ocabula y (Godwin-Jones,2018), and
NiinMik¨
aOli?! aims o acili a e his in he con-
ex o web pages. NiinMik¨
aOli?! can be clas-
si ied as an ATICALL (Au hen ic Tex In elligen
Compu e Aided Language Lea ning) sys em, de-
ined by Meu e s e al. (2010a) as so wa e which
p oduces enhanced inpu based on eal ex s.
The ocus o his pape is upon NiinMik¨
aOli?!’s
simpli ied, uni ied iew o he Finnish language,
which uses in o ma ion isualisa ion echniques o
“show a he han ell” lea ne s abou mo pholog-
ical and wo d o ma ion ea u es. A p inciple aim
This wo k is licensed unde a C ea i e Commons A i-
bu ion 4.0 In e na ional Licence. Licence de ails: h p:
//c ea i ecommons.o g/licenses/by/4.0/.
is o a oid p esen a ions which ell lea ne s abou
wo d-le el g amma ical ea u es such as echnical
linguis ic language including La ina e names o
nominal cases, a he op ing o highligh hei su -
ace o ms. The use in e ace isualises he con-
nec ion be ween su ace o ms, analy ic o ms and
de ini ions.
Desc ibed i s is he cons uc ion o he base-
line eading assis an sys em. The sys em is
by and la ge simila o exis ing sys ems such as
GLOSSER (Ne bonne e al.,1998) o he eading
assis an ea u es o SMILLE (Zilio e al.,2017)
o Re i a (Ka inskaia e al.,2017) – o e en e y
widely used sys ems such as he Wo dNe -based
al e na i e ansla ions shown when a single wo d
is selec ed in Google T ansla e. No able as an
imp o emen o e some o hose sys ems is Ni-
inMik¨
aOli?!’s use o a ull scale Wo d Sense Dis-
ambigua ion (WSD) sys em. The es o his pape
desc ibes he mo i a ion behind and implemen a-
ion o NiinMik¨
aOli?!’s isualisa ions and i s ex-
haus i e ea men o complex lexical i ems such
as de i ed wo ds, compounds and mul iwo d ex-
p essions.
2 Baseline sys em
A combined lexical esou ce o Finnish was c e-
a ed by combining wo sou ces: FinnWo dNe
(Lind´
en and Ca lson,2010) and Wik iona y. The
Wik iona y de ini ions we e ex ac ed om pub-
licly a ailable dumps, using a Py hon sc ip 1. The
Py hon sc ip pa ses MediaWiki ma kup in o wo d
senses using mwpa se omhell2.
A leas one de ini ion was ex ac ed om
99.8% o a o al o 153 196 Wik iona y pages con-
1A ailable a h ps://gi hub.com/ ankie /
wikipa se.
2h ps://gi hub.com/ea wig/
mwpa se omhell
F ankie Robe son 2020. Show, don’ ell: Visualising Finnish wo d o ma ion in a b owse -based eading
assis an . P oceedings o he 9 h Wo kshop on Na u al Language P ocessing o Compu e Assis ed Language
Lea ning (NLP4CALL 2020). Linköping Elec onic Con e ence P oceedings 175: 37–45.
37
aining Finnish as a sec ion heading3. O hese,
90 653 a e lemmas a he han in lec ed o ms. Fo
compa ison, FinnWo dNe con ains 139 871 head-
wo ds, which a e mos ly lemmas bu include oc-
casional idioma ic wo d o ms such as humalassa
(li e ally “in hops”).
FinnWo dNe is modelled a e Wo dNe , and
as such has e y ine-g ained sense dis inc-
ions. This esul s in po en ially o e whelming he
lea ne wi h oo much in o ma ion. Fu he mo e,
some Wik iona y senses a e likely o essen ially
duplica e FinnWo dNe . Thus, simila de ini ions
should be clus e ed oge he and only he bes de -
ini ion displayed by de aul .
The clus e ing and alignmen was c ea ed using
a ini y p opaga ion (F ey and Dueck,2007). The
dis ances g aph is cons uc ed by aking cosine
dis ances be ween de ini ions, ep esen ed as ec-
o s based on he English ex o hei de ini ions
acco ding o he English sen ence simila i y model
o Reime s and Gu e ych (2019). This model is
based on p e ained English BERT models ine-
uned on a seman ic simila i y ask. The p e ained
be -la ge-nli-s sb-mean- okens
model is used. Links be ween Wik iona y de ini-
ions wi h dis inc e ymologies a e hen emo ed
and ex a weigh is gi en o Wik iona y de ini ions
so as o encou age hem o become exempla s o
clus e s. The esul ing sys em ob ains adjus ed
and index sco es o 0.48 on a gold s anda d
o Wo dNe e bs g ouped by P opBank sense
ob ained om P edica e Ma ix (de Lacalle e al.,
2016), and a sco e o 0.52 on a manually c ea ed
clus e ing o 128 Wik iona y and Wo dNe noun
de ini ions. The sciki -lea n (Ped egosa e al.,
2011) implemen a ion o a ini y p opaga ion is
used.
WSD is pe o med using UKB (Agi e e al.,
2014). Since UKB is a g aph based WSD al-
go i hm, i only ope a es on de ini ions om
FinnWo dNe , which a e connec ed by he se-
man ic links om P ince on Wo dNe . In o de
o compa e Wo dNe de ini ions wi h Wik iona y
de ini ions, he clus e ing is used. Clus e s a e
hen anked using hei bes Wo dNe de ini ion as
a ep esen a i e. Since Wik iona y de ini ions a e
usually be e o lea ne s, hey a e pushed o he
op o each clus e in he use in e ace.
3In a Wik iona y dump om 6/4/2019.
100101102103104105106107
Rank
0.0
0.2
0.4
0.6
0.8
1.0
P opo ion
Remaining p opo ion
Compounds pe oken
Figu e 1: P opo ions ela ed o wo ds unknown o a
simpli ied model o a language lea ne . The x-axis gi es
he ank o he wo d ha he lea ne has lea ned all wo ds up
o. Remaining p opo ion is he p opo ion o wo ds in un-
ning ex unknown o he language lea ne . Compounds pe
oken is he p opo ion o unknown wo ds which a e com-
pounds.
3 Linguis ic a ionale
Finnish is mo phologically ich4. Subs an i es
a e declined o case and numbe and e bs a e
conjuga ed o pe son, ense and oice. Finnish
wo d o ma ion is also ich5. I includes a numbe
o highly p oduc i e de i a ional mo phemes, in-
cluding many de e bal mo phemes which is cha -
ac e is ic o he language. Compounding also
plays a majo ole in Finnish wo d o ma ion, wi h
many o he compounds being seman ically ans-
pa en . Finnish also has a numbe o encli ic
pa icles such as he ques ion o ming “-ko/-k¨
o”.
Finally, i has MWEs (Mul i-Wo d Exp essions)
such as idioms. In Finnish hese may ake he o m
o syn ac ic ames, ea ed he e as gapped MWEs
e.g. “pi ¨
a¨
a-s a”, which could occu in a o m
such as “pid¨
an oileip¨
akakus a” (English: I like
sandwich cake) dis inguished om e.g. “pid¨
an
oileip¨
akakun” (English: I keep sandwich cake).
Why bo he going o he e o o making a
comp ehensi e ea men o wo d o ma ion and
complex wo d ypes? A e all, hese lexical i ems
occu ela i ely in equen ly in unning ex and
so i may seem like a poo alloca ion o e o o
spend ime dealing wi h hem. One assump ion
he e is ha hese elemen s become mo e impo -
an a e he beginne s age o language lea n-
ing. I we assume a e y simpli ied model o
lexical acquisi ion whe e wo ds a e lea n in de-
4See o example Ka lsson (2015).
5See o example Hy ¨
a inen (2019).
P oceedings o he 9 h Wo kshop on Na u al Language P ocessing o Compu e Assis ed Language Lea ning (NLP4CALL 2020)
38
scending o de o equency, we can analyse p op-
e ies o wo ds ha he language lea ne does no
know and he e o e may like o look up. Figu e 1
shows wo such p ope ies a ying as he numbe
o wo ds he lea ne knows inc eases: he p opo -
ion o all wo ds seen which a e unknown, and
he p opo ion o unknown wo ds which a e com-
pounds. The da a is based on 1.5 billion okens
o analysed Finnish ex om he Tu ku In e ne
Pa sebank (Laippala and Gin e ,2014). Taken as a
whole, he co pus is 9.8% compounds. Supposing
ha an in e media e lea ne may know somewhe e
be ween 1000 and 10 000 wo ds. A e lea ning
1000 wo ds, 24% o unknown wo ds would be
compounds, and a e 10 000, i would be 42%.
Thus qui e a la ge p opo ion o wo ds unknown
o in e media e le el lea ne s a e compounds. I is
assumed ha o he complex lexical i ems such as
MWEs ollow a simila pa e n. He e we e e o
any i em which can be gi en a de ini ion, includ-
ing lemmas, indi idual mo phemes and MWEs as
headwo ds.
Admi ing hese i ems a e equen , he nex
ques ion becomes, why is simple lemma ex-
ac ion no su icien ? One a gumen agains
pe o ming lemma ex ac ion and simply show-
ing lemmas comes om he no icing hypo he-
sis (Schmid ,1990) which s a es ha wi hou a -
en ion o o m (Ligh bown and Spada,2013,
pp. 168–175), second language lea ne s in pa ic-
ula a e p one o no acqui ing ine-g ained g am-
ma ical knowledge. Following his concep , sys-
ems such as hose o Meu e s e al. (2010b) and
Reynolds e al. (2014) we e c ea ed o au oma i-
cally enhance inpu in web pages in o de o p o-
mo e no icing o , o example, pa s o speech. Ni-
inMik¨
aOli?! ollows a simila di ec ion, bu in-
s ead ocusses on mo phemes, d awing a en ion
o he connec ion and o e lap be ween analy ic
and su ace o ms, so o p omo e lea ning o mo -
phology, as well as a en ion o he o ma ion o
he wo d i sel .
Why analyse wo ds using only no malised seg-
men s, a he han — as a lo o e e ence ma e-
ial o he Finnish language does — using g am-
ma ical desc ip ions, such as La ina e names o
case endings. The eason o his is wo old.
Fi s ly, as Bleyhl (2009) no es, ea men s o lan-
guage which a e hea y on g amma ical analysis
and he associa ed linguis ic e minology can be
coun e -p oduc i e in language ins uc ion since
hey d aw a en ion away om he comp ehen-
sible inpu needed o ue language acquisi ion.
This la ge amoun o ex a ma e ial can lead o
educed con idence om lea ne s. Secondly, due
o Finnish’s agglu ina i e mo phology, con as ed
wi h a usional language like La in, i is simply
no necessa y o add his ex a laye o analy i-
cal language, since many Finnish in lec ional mo -
phemes occu in he same o m o an easily ecog-
nisable o m a all imes, and hey can hus be e-
e ed o by hei no malised o m. Conside o
example, he Finnish sys em o loca i e case end-
ings. These ha e a ai ly good co espondence
in e ms o unc ion wi h p eposi ions in English.
Imagine i , when eaching English, e e y p eposi-
ion was also gi en a name o desc ibe i so ha
we would always e e o “ om” as “ he ela i e
p eposi ion”. I is ha d o imagine ha a lea ne
would be well se ed by his ex a indi ec ion!
This p inciple is somewha lexible, howe e , and
he names o he mos common case endings —
pa i i e and geni i e — a e shown on he basis
ha hei usage is mo e g amma ical. They a e
mo e o en obliga ed by con ex a he han used
wi h he in en ion o con eying ex a in o ma ion.
Plu al is e e ed o by-name since i is likely o be
amilia .
4 Implemen a ion
The pipeline om unning ex o analy ical seg-
men s, desc ibed in his sec ion, is shown in Fig-
u e 2.
4.1 MWE lexicon & ex ac ion
FinnMWE (Robe son,2020) is used as a lexicon
o MWEs. In o de o ex ac MWEs om unning
ex , o each MWE is indexed by all possible lem-
mas o a key oken. In case he head is known, i
is used as he key oken, o he wise he a es o-
ken based on wo d eq (Spee e al.,2018) is used.
MWEs a e hen ex ac ed om dependency ees
ob ained using he Tu ku neu al dependency pa s-
ing pipeline (Kane a e al.,2018). Fi s , all key
ma ches a e ound simul aneously by looking up
all lemmas in he dependency ee. These candi-
da es a e il e ed by ying o ma ch each emain-
ing oken in he MWE agains any neighbou , ex-
ending he neighbou hood in he p ocess un il he
whole MWE is ma ched o i is impossible o p o-
ceed. When he MWE key is i s head, i s pa en
is excluded om he neighbou hood o po en ial
P oceedings o he 9 h Wo kshop on Na u al Language P ocessing o Compu e Assis ed Language Lea ning (NLP4CALL 2020)
39
Running ex
Token
Dependency ee
MWE
Headwo d
Analy ical segmen
Tokenisa ion Tu ku-neu al-
pa se -pipeline
Reuse
ancho
Reuse
ancho
Analy ical
segmen a ion
s ep
Ex ac ion
Reuse
ancho
Spli
Fo ce-Align
Analy ical
segmen a ion
s ep
Fo ce-Align
Figu e 2: Diag am showing p ocessing pipeline om
su ace o ms o analy ical segmen s. Objec s indica ed
in yellow a e linked wi hin he use in e ace du ing he
ho e b ushing in e ac ion. Do ed lines indica e how spans
co esponding o he in-node a e ound in he ou -node.
ma ching okens. MWE okens wi hou a lemma
ac as wildca ds, and can ma ch mul iple okens,
bu hey mus be connec ed wi hin he dependency
ee.6
4.2 Analy ical segmen a ion da a
The app oach o analy ical segmen a ion pu -
sued he e is o combine analyses om he
Omo i mo phological analyse (Pi inen,2015)
and in o ma ion om Wik iona y oge he o
p oduce analy ical segmen a ions. Omo i p o-
duces analyses in i s own o ma , which
has some deg ee o compa ibili y wi h ags
om he Uni e sal Dependencies (UD) p ojec
(Pyysalo e al.,2015). As an example,
kakus a may be analysed as [WORD ID=kakku]
[UPOS=NOUN][NUM=SG][CASE=ELA]. Mo -
phological ags a e mapped o analy ical mo -
phemes so ha e.g. [CASE=ELA] is ou pu as
-s a, while WORD ID is passed h ough. The o -
de in which he ags appea is he same o de as
he su ace mo phemes appea , meaning ou an-
aly ical mo phemes a e in he same o de as he
6The MWE ex ac ion code is made a ailable a h ps:
//gi hub.com/ ankie /lex ac
su ace mo phemes.
Wik iona y con ains a ious empla e ags
which gi e in o ma ion abou wo d o ma ion.
This da a is sc aped in o a da abase so ha each
e ymology sec ion can gi e a de i a ion o i s
headwo d. Templa e ags a e no malised in o ei-
he in lec ions, de i a ions o compounds consis -
ing o no malised segmen s. Fo compounds and
mos de i a ions, no malised segmen s a e di ec ly
a ailable as a gumen s o he empla e ag. How-
e e , he agen noun o empla e ag, o ex-
ample, mus be manually mapped o “-ja”. Finally,
he o m o empla e ag makes use o g am-
ma ical e ms such as ela i e, which a e mapped
o no malised segmen s such as “-s a”.
4.3 Building segmen a ion de i a ions
Complex wo ds may ha e se e al le els o com-
pounding o wo d de i a ion and in lec ion. Thus,
we may ha e o make use o se e al lookups o
ully segmen a wo d o m. We also wan o make
su e a comple ely segmen ed wo d o m can be as-
socia ed wi h all lexical i ems ha make i up. We
hus shi ou pe spec i e o hink o hese analyses
as ules and he segmen e as a ule engine which
applies hem o p oduce de i a ions subjec o con-
s ain s. Each ule can ma ch any single segmen
and p oduce many segmen s.
The basic ule engine ope a es by ecu si ely
applying ules. I keeps ack o he cu en on
o he de i a ion ee. A each i e a ion, each node
om he on is conside ed and one o mo e s eps
consis ing o applying one o mo e ules a e aken
o c ea e child nodes, c ea ing a new on . The e
may be mul iple ules which can ma ch a segmen .
In his case, all combina ions o ules ma ching
each ma chable segmen a e applied. When ei he
he e a e no mo e ules which ma ch, o he e is
a ma ch which does no expand any segmen s, he
node is ma ked as e minal.
A simple app oach would be o allow all ules
o apply a once. Howe e , Omo i analyses do no
wo k e y well as ules as-is in ou case, o ex-
ample o oileip¨
akakus a Omo i p oduces h ee
di e en analyses o di e en le els o decom-
pounding o oileip¨
akakku. I we we e o apply
each o hese analyses as ules we would end up
wi h 3 inal segmen a ions. Howe e , o ou pu -
poses, hey should all be subsumed unde he same
de i a ion. The e o e we ake he ollowing ap-
p oach:
P oceedings o he 9 h Wo kshop on Na u al Language P ocessing o Compu e Assis ed Language Lea ning (NLP4CALL 2020)
40

1. Fi s , apply Wik iona y based ules ecu -
si ely.
2. Fe ch all Omo i ules esul ing om looking
up he whole wo d o m
3. While he e a e Omo i ules le :
(a) Remo e any Omo i ules al eady sub-
sumed by he cu en de i a ion.
(b) I any ules emain, apply he one p o-
ducing he leas new segmen s and dis-
ca d.
(c) Apply Wik iona y based ules ecu -
si ely.
4. Apply any e o i ing ules, which exis o
deal wi h occasional cases o usional Finnish
mo phology.
Fo example oileip¨
akakus a (English: ou
o / om sandwich cake) would p oduce he ollow-
ing de i a ion:
Example 1: oileip¨
akakus a
→ oileip¨
akakku s a Omo i: oileip¨
akakus a
→ oileip¨
a kakku s a Wik iona y: oileip¨
akakakku
→ oi leip¨
a kakku s a Wik iona y: oileip¨
a
While oimakkaammin (English: mo e powe -
ully) would p oduce he ollowing:
Example 2: oimakkaammin
→ oimakkaas i mpi Wik iona y: oimakkaammin
→ oimakas s i mpi Wik iona y: oimakkaas i
→ oima kas s i mpi Wik iona y: oimakas
→ oida ma kas s i mpi Wik iona y: oima
In his case a e o i ing ule mmin →s i mpi7can
be applied:
Example 3: oimakas s i mpi
← oimakas mmin Re o i : mmin
← oimakkaammin Connec o pa en
4.4 Cons ain s upon ules
Applied as-is, his scheme will p oduce impossi-
ble segmen a ions. Howe e , i we conside he
POS (Pa -O -Speech) o each segmen , we can
place cons ain s o amelio a e his.
We use a simple se o POS ags based on Wo d-
Ne : Ve b, Noun, Ad e b, Adjec i e & Unknown.
The UD POS ags used by Omo i and he Wik-
iona y POS headings a e mapped in o his com-
mon scheme. The mapping is lossy, o example,
UD adposi ions a e mapped on o he Ad e b POS.
All closed classes, in e jec ions and a ixes a e
mapped o Unknown. No e ha cons i uen wo ds
o Finnish compounds can be in lec ed wo ds, and
7“-s i” is ad e b o ming mo pheme like “-ly”, while “-
mpi” is a compa a i e o ming mo pheme like “-e ”.
oimakkaammin
o i m a k a s m m i n
o i m a k a s s i m p i
o i d a m a
cos : 13
cos : N/Acos : 0
cos : 4
Figu e 3: Alignmen wi hin de i a ion ee o
oimakkaammin. Da k yellow po ions deno e su ace
spans, while each o he whole yellow po ions including
da k and ligh deno e he whole logical span. The cos
o each alignmen acco ding o Fo ce-Align is shown nex o
he pa en segmen . The dashed lines indica e he alignmen
is no p oduced by Fo ce-Align bu ins ead ob ained om he
unde lying ule. In his case: he syn he ic ule -mmin →-s i
-mpi.
so he e in lec ed o ms a e ea ed as ha ing he
POS o hei lemma.
The pe missible compound POS pa e ns can
hen be p oduced by a lis o p oduc ion ules, ob-
ained by s udying Hy ¨
a inen (2019):
Ve b →Noun Ve b (e.g., koe+len ¨
a¨
a)
Ve b →Ad e b Ve b (e.g., edes+au aa)
Noun →Noun Noun (e.g., oi+leip¨
a)
Noun →Adjec i e Noun (e.g., puna+ iini)
Adjec i e →Adjec i e Adjec i e (e.g., hy ¨
an+n¨
ak¨
oinen)
We s a by ea ing he whole oken as ha ing Un-
known POS, meaning we can ma ch any POS. A
any ime a segmen is cons ained o ha ing one o
a se o POS ags. Fo compounds o mo e han
wo pa s, we can ob ain he possible POS pa e ns
by expanding he p oduc ion ules gi en abo e.
Re e ing back o Example 1, a Wik iona y ule
allows he analysis o oi (Ve b) as oida (3 d
pe s.), howe e oileip¨
akakku is known o be a
Noun, meaning acco ding o he abo e ules, oi
mus be ei he a Noun o an Adjec i e, meaning
his ule canno be applied.
4.5 Ob aining alignmen s om de i a ions
In o de o ind co espondences be ween indi-
idual cha ac e s in analy ical segmen s, su ace
o ms and headwo ds, we apply he Fo ce-Align
p ocedu e a each s ep o he analy ical segmen-
a ion de i a ion. Each child segmen is gi en a
span in o he pa en segmen . These a e o de ed
and non-o e lapping. Ma ches a e pe o med a -
P oceedings o he 9 h Wo kshop on Na u al Language P ocessing o Compu e Assis ed Language Lea ning (NLP4CALL 2020)
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Func ion FORCE-ALIGN(pa en s ing p, a ay o
child s ings c1. . . cn)
e u ns alignmen a
C ea e a bounded p io i y queue pq wi h he
lowes cos pa ial solu ion a i s on
Add an emp y pa ial solu ion in o pq
while he head o pq is no comple e do
Pop pa ial solu ion j om on o pq
/*Make a ma ch */
i j’s cu so in o chas no eached end and
j’s cu so in o phas no eached end and
p(j’s cu so in o p)=c(j’s cu so in o c)
hen
Add copy o jin o pq wi h i s cu so s
in o pand cinc emen ed
end
/*Skip a pa en cha ac e */
i j’s cu so in o pis no a beginning o
end hen
Add copy o jin o pq wi h i s cu so
in o pand i s pa en cha ac e s
skipped inc emen ed
end
/*Skip he es o he
cu en child segmen */
i j’s cu so in o pis no a beginning and
j’s cu so in o chas no eached end hen
Add copy o jin o pq wi h i s cu so
in o cand i s child cha ac e s
skipped inc emen ed
end
end
a:= alignmen o med by solu ion a on o pq
end
Algo i hm 1: The Fo ce-Align p ocedu e o ind
an alignmen be ween a pa en s ing and i s seg-
men ed child en s ings.
e no malisa ion. All s ings a e lowe cased and
he on owels ¨
a, ¨
o and y a e mapped o he
espec i e back owels a, o and u. We aim o
minimise a cos de ined as he sum o he squa e
o he numbe o pa en cha ac e s skipped and
squa e o he numbe o child cha ac e s skipped.
An example showing he ype o alignmen s p o-
duced by Fo ce-Align applied o he de i a ion o
oimakkaammin is shown in Figu e 3.
Fo ce-Align is implemen ed as a dynamic p o-
g amming s yle p ocedu e, gi en as pseudocode
in Algo i hm 1. A each s ep, Fo ce-Align keeps
ack o candida e solu ions in a p io i y queue,
wi h he lowes cos pa ial solu ion always being
a he on . The p io i y queue has bounded leng h
o bound he unning ime — making he p oce-
du e a o m o beam sea ch. Whene e a pa ial
candida e solu ion is aken om he on o he
queue, up o h ee new pa ial solu ions a e c e-
a ed and added back o he p io i y queue: making
a single cha ac e ma ch; skipping a single cha -
ac e om he pa en s ing; and skipping he es
o he cha ac e s in he cu en child segmen . The
p ocedu e ends when he e is a comple e solu ion
a he on o he queue. Each child segmen ’s
ull span co e s he cha ac e s om he beginning
o i s i s cha ac e ma ch o jus be o e he i s
cha ac e ma ch o he nex child segmen , o un il
he end o he pa en segmen in he case o he las
child segmen .
A span o a segmen on o any ances o seg-
men can be ound by ollowing a simple ule a
each s ep: shi he whole span igh wa ds by a
enough o i any new segmen s o he le , and ex-
end he igh edge o he end o he child span
on o he pa en segmen while he cu en child
segmen is he igh mos . As an example, con-
side Figu e 3and he analy ical mo pheme -kas.
I begins on he igh edge. We conside i s align-
men on o oimakas and ind we mus shi i s le
edge by i e cha ac e s o make space o oima.
We eplace i s igh edge wi h ha o i s pa en ,
which does no change he span. -kas is s ill on
he igh edge o oimakas when we conside he
alignmen o oimakas and oimakkaammin. A
his s ep, we do no ha e o shi he le edge since
he e a e no new segmen s o he le . The igh
edge is eplaced wi h he igh edge o he align-
men o oimakas on o oimakkaammin, shi ing
i igh by one cha ac e . The inal span con ains
he cha ac e s ‘kkaa’ — which is he allomo ph
co esponding o ‘kas’, as equi ed.
When he use ho e s o e a segmen o a seg-
men a ion in he use in e ace, he co esponding
su ace o m o he same segmen should high-
ligh . The highligh ing consis s o a s ong high-
ligh o ha pa o he su ace o m which has
o e lapping ex wi h he analy ic o m, and a
weak highligh o ha pa which is g amma i-
cally pa o he same mo pheme bu does no li e -
ally ma ch. The s ong highligh is ound by ind-
ing he longes ma ch be ween he child segmen
and i s span wi hin he pa en segmen , while he
weak highligh is made om any pa o he span
which is le o e .
Special conside a ion is gi en o wildca ds,
such as -s a. In his case, ma ching is pe o med
igh o le , and he wildca d is weakly ma ched
agains ha which emains a e all o he analy ic
segmen s a e aligned.
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Figu e 4: A sc eensho showing he Finnish Wikipedia
page Tu ku being ead using he b owse ex ension.
5 Visualising Finnish wo d o ma ion
A sc eensho o he use in e ace is shown in Fig-
u e 4. De ini ions a e g ouped by no malised seg-
men a ion. Wi hin each no malised segmen a ion
he e a e de ined headwo ds, each co esponding
o one o mo e o he no malised segmen s. They
a e o de ed in dec easing o de o co e age o he
no malised segmen a ion, meaning hose de ini-
ions which de ine he meaning o he su ace o m
mos closely appea closes o he op. Wi hin each
de ined headwo d appea s one o mo e clus e s o
de ini ions, each wi h an exempla .
To b ing a en ion back o su ace o ms om
he no malised o ms, he in e ace highligh s he
su ace o ms as he lea ne ho e s o e he seg-
men ed o ms, as shown in Figu e 5. The in e ac-
ion ecalls a one dimensional “ho e sc ub” ac-
ion. Ini ially, he whole wo d o ph ase is ligh ly
highligh ed. As he lea ne sc ubs o e analy ic
mo phemes, he co esponding spans in he su -
ace o m a e highligh ed.
To show he connec ion be ween he no malised
segmen a ion and i s de ini ions, pa s o he de-
ined headwo ds a e highligh ed when no malised
segmen s a e ho e ed o e , as shown in Fig-
u es 4&5. The whole in e ac ion se es o
link he di e en iews o su ace o m, analy i-
cal o m and headwo ds.8
6 Conclusions and Fu u e Wo k
This pape p esen ed NiinMik¨
aOli?! The sys-
em s eamlines he expe ience o using e e ence
ma e ial by p esen ing i in-con ex , emphasising
he mos impo an pa s and p esen ing simpli ied
g amma ical analyses which do no ely upon ech-
8The NiinMik¨
aOli?! b owse ex ension and websi e
a e a ailable a h ps://niinmikaoli. i/, while he
analy ical segmen a ion code is a ailable a h ps://
gi hub.com/ ankie /asa i.
Figu e 5: A composi e sc eensho showing di e en
s ages o he in e ac ion esul ing when a use b ushes
o e segmen s in he ex analyse .
nical linguis ic ja gon, ollowing he p inciple o
“show, don’ ell”.
Clea ly, he ques ion o whe he sys ems such
as NiinMik¨
aOli?! uly help language lea ne s is a
pe inen one. Quan i a i e use e alua ion o ali-
da e exis ing ea u es and poin o new ones is hus
an impo an piece o u u e wo k.
NiinMik¨
aOli?! gi es de ini ions in English.
Adding common L1 languages o Finnish lea n-
e s such as Swedish, Russian o A abic, as well as
Finnish i sel could be a use ul addi ion.
The cu en analy ical segmen e is ule based,
and hus canno handle ou o ocabula y wo ds.
A machine lea ning app oach such as ha o Kann
e al. (2016) could be combined wi h he da a de-
eloped he e o add ess his.
A u u e di ec ion o all eading assis an s is
be e p edic ion o language lea ne needs, which
would lead o a sys em which knows be o ehand
which ypes o eading assis ance would be bes
o o e ei he by explici ly eques ing in o ma ion
om he lea ne , o implici ly using in o ma ion
om p e ious in e ac ions wi h he so wa e.
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