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In e na ional Jou nal o Ad ance and Applied Resea ch
www.ijaa .co.in
ISSN – 2347-7075
Impac Fac o – 8.141
Pee Re iewed
Bi-Mon hly
Vol. 6 No. 38
Sep embe - Oc obe - 2025
B idging Linguis ic Di e si y using Uni ied NLP Toolki o Indian Languages
D . Jyo i R. Jadha
Indi a Uni e si y School o In o ma ion Technology Pune
Co esponding Au ho –D . Jyo i R. Jadha
DOI - 10.5281/zenodo.17315879
Abs ac :
India has a wide a ie y o languages, bu many o hem a e no well-suppo ed by cu en
echnology. This is because he e a en' enough digi al esou ces and he languages hemsel es a e
complex. This pape in oduces a new, comp ehensi e NLP oolki speci ically designed o add ess his
p oblem. The oolki is buil wi h a modula design and includes ea u es ha adap o he unique
cha ac e is ics o each language, as well as ea u es ha help ans e knowledge be ween languages.
Ou es ing shows ha his oolki is no only mo e e icien and easie o use bu also signi ican ly
imp o es he pe o mance o key asks like okeniza ion (b eaking down ex in o wo ds) and machine
ansla ion. We a e eleasing his oolki as an open-sou ce p ojec so ha i can become a undamen al
ool o de elope s and esea che s wo king on Indian languages.
In oduc ion:
In essence, his passage explains ha
while echnologies like Na u al Language
P ocessing (NLP) ha e seen signi ican
p og ess o majo languages, Indian
languages ha e lagged behind. This is due o a
ew key p oblems: hey o en ha e complex
s uc u es and limi ed digi al esou ces, and
he e's a lack o s anda dized ools and
o ganized da a. The main pu pose o he pape
is o in oduce a single, comp ehensi e NLP
oolki designed speci ically o o e come hese
hu dles. The oolki is buil o be lexible and
wo k o di e en languages, ac ing as a
cen al pla o m o all majo NLP asks. This
includes e e y hing om p epa ing ex o
analysis o unde s anding he meaning and
ansla ing i , all wi hin one uni ied sys em.
Indian languages ace signi ican challenges in
he wo ld o NLP due o hei unique
cha ac e is ics and he lack o digi al
esou ces. While globally dominan languages
like English and Manda in ha e bene i ed
om ex ensi e esea ch and la ge da ase s,
many o India's languages a e mo phologically
ich, meaning wo ds can ha e complex
in e nal s uc u es, and a e conside ed low-
esou ce, wi h e y ew digi al ex s a ailable
o aining NLP models. This is u he
complica ed by a lack o s anda dized ools
and uni ied amewo ks, which makes i
di icul o build consis en and e ec i e NLP
applica ions. This new oolki aims o sol e
hese p oblems by p o iding a modula ,
scalable, and language-agnos ic pla o m. I s
design allows di e en componen s o be
easily in eg a ed o swapped ou , making i
lexible o a ious asks. The oolki b ings
oge he capabili ies o p e-p ocessing (like
cleaning and okenizing ex ), syn ac ic
analysis (unde s anding sen ence s uc u e),
seman ic unde s anding (in e p e ing
meaning), and machine ansla ion in o a
single, cohesi e amewo k. This app oach is
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D . Jyo i R. Jadha
298
designed o c ea e a ounda ional esou ce ha
can be adap ed and ex ended o he di e se
linguis ic needs o India.
Li e a u e Re iew:
While a ious e o s exis o ad ance
Na u al Language P ocessing (NLP) o Indian
languages, hey a e o en agmen ed and
limi ed in scope. Founda ional lib a ies like
he IndicNLP Lib a y o e basic ools o a
ew languages, and ini ia i es om g oups like
AI4Bha a ha e made p og ess wi h la ge-
scale models like IndicBERT and IndicT ans.
Howe e , hese a e o en isola ed p ojec s
a he han comp ehensi e solu ions. Gene al-
pu pose NLP ools such as NLTK and spaCy,
while powe ul o o he languages, don'
p o ide adequa e suppo o he speci ic
complexi ies o Indian languages. The
challenge is ha mos o hese exis ing
app oaches all sho in one way o ano he .
Olde ule-based sys ems a e linguis ically
de ailed bu don' scale well o new da a o
languages. On he o he hand, mode n
ans o me -based models like mBERT and
XLM-R, while mul ilingual, o en s uggle
wi h he unique cha ac e is ics o Indian ex ,
especially when di e en languages a e mixed
oge he (code-mixing) o when he e's e y
li le da a a ailable (low- esou ce scena ios).
This collec i e lack o comp ehensi e
co e age and modula design highligh s a
clea need o a new, uni ied amewo k ha
can be easily ex ended and adap ed o mee he
ull ange o linguis ic challenges in India.
Many Indian languages a e mo phologically
ich, meaning a single wo d can con ey a lo
o in o ma ion h ough i s s uc u e. Unlike
English, whe e you migh add a sepa a e wo d
like "wen " o "will go," Indian languages
o en use su ixes o indica e ense, gende ,
numbe , and case. Fo ins ance, in Hindi, he
e b oo jaa- ( o go) can ans o m in o jaa ā
hai (he goes), jaa ī hai (she goes), o jaa e
hain ( hey go) jus by changing he ending.
This makes i di icul o NLP models o
ecognize he base o m o a wo d and i s
a ious g amma ical unc ions, equi ing much
mo e sophis ica ed analysis han simple wo d-
spli ing. Code-mixing is he p ac ice o
blending wo o mo e languages wi hin a
single con e sa ion o sen ence. This is
inc edibly common in India, whe e a speake
migh use English wo ds o ph ases while
speaking a egional language. Fo example, a
sen ence migh be, "I’m going o he ma ke ,"
whe e "ma ke " is an English wo d in eg a ed
in o a Hindi o Bengali sen ence. This poses a
majo challenge o NLP models because hey
a e ypically ained o p ocess one language a
a ime. The mix o ocabula y, g amma , and
e en sc ip s (e.g., using Roman sc ip o an
Indian wo d) can con use models, leading o
e o s in asks like pa -o -speech agging,
sen imen analysis, and machine ansla ion.
Me hodology:
The design o he NLP oolki o
Indian languages is guided by ou key goals.
Fi s , i aims o b oad language co e age,
wi h an ini ial ocus on suppo ing a leas 10
o India's majo languages. This ensu es he
oolki isn' limi ed o jus a ew, bu can se e
a wide use base. Second, he amewo k is
buil wi h modula i y in mind, meaning i
consis s o sepa a e, in e changeable
componen s o speci ic asks like okeniza ion
(spli ing ex in o wo ds), POS agging
(iden i ying pa s o speech), named en i y
ecogni ion (NER), and machine ansla ion
(MT). This modula design allows use s o
selec and combine only he ools hey need.
Thi d, he oolki emphasizes ex ensibili y,
allowing use s o easily in eg a e hei own
cus om models and da ase s. This ensu es he
pla o m can g ow and adap wi h new
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D . Jyo i R. Jadha
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esea ch and applica ions. Finally, he p ojec
is open-sou ce, which encou ages communi y-
d i en de elopmen and allows o anspa en
e alua ion o i s pe o mance and ea u es.
Da a Collec ion and Cu a ion:
1. Co pus Selec ion: Begin by speci ying
he languages you will be using o he
s udy. Jus i y you selec ion. Fo example:
"Ou s udy ocuses on a ep esen a i e se
o ou majo Indian languages: Hindi and
Ma a hi (Indo-A yan amily, De anaga i
sc ip ), and Tamil and Telugu (D a idian
amily, dis inc sc ip s). This selec ion
allows us o es he oolki 's adap abili y
ac oss di e en language amilies and
o hog aphies."
2. Da a Sou ces: De ail he sou ces o you
da a. A e you using public da ase s (e.g.,
om pla o ms like Hugging Face, o
academic p ojec s like he IndicCo p
da ase )? A e you sc aping da a om
speci ic websi es (e.g., news a icles,
social media)?
3. Da a P e-p ocessing: Explain he s eps
aken o p epa e he aw da a. This is a
c ucial pa o NLP me hodology.
4. No maliza ion: Desc ibe how you handle
a ia ions in spelling, capi aliza ion, and
punc ua ion.
5. Tokeniza ion: Explain he okeniza ion
s a egy. A e you using a subwo d-based
app oach like Wo dPiece o
Sen encePiece? Jus i y why a mul ilingual
o uni ied okenize is essen ial o you
oolki .
6. Handling Mul ilingualism: De ail how
you manage code-mixing and language
iden i ica ion wi hin he co pus.
Pu posed Model o Uni ied NLP Toolki o Indian Languages
The P e- aining is he BERT model
lea ns he undamen al ules o language
wi hou human supe ision. I 's a massi e,
esou ce-in ensi e p ocess ha happens only
once.
1. Inpu : The model is ed as amoun s o
unlabeled ex , such as millions o books
o web pages. This aw ex is b oken
down in o sen ences, and pai s o
sen ences a e ed in o he model.
2. Two Unsupe ised Tasks: To o ce he
model o lea n abou language, BERT is
gi en wo dis inc " ill-in- he-blanks"
asks:
3. Masked Language Model (Mask LM): The
model andomly masks (hides) abou 15%
o he wo ds in he inpu sen ences. The
goal is o he model o p edic he o iginal
masked wo ds based on he con ex o he
wo ds su ounding hem. This is a c ucial
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D . Jyo i R. Jadha
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ask because i o ces he model o lea n a
deep, bidi ec ional unde s anding o
language (looking a wo ds o he le and
igh ).
4. Nex Sen ence P edic ion (NSP): The
model is gi en wo sen ences, "Sen ence
A" and "Sen ence B," and has o p edic
whe he "Sen ence B" is he ac ual nex
sen ence ha ollows "Sen ence A" in he
o iginal ex . This ask helps he model
unde s and ela ionships be ween
sen ences, which is i al o asks like
ques ion answe ing and documen
summa iza ion.
Fine- uning is he p e- ained BERT
model is adap ed o sol e a speci ic,
downs eam ask. This s age is much as e
and equi es signi ican ly less da a.
1. Reusing he P e- ained Model: The p e-
ained BERT model is used as a
ounda ion. I s lea ned knowledge ( he
encoded ep esen a ions) is kep , bu he
ou pu laye is modi ied o i he new
ask. The co e BERT model is essen ially
a " ea u e ex ac o " o he new ask.
2. Task-Speci ic Inpu : The model is now ed
a much smalle , labeled da ase o a
speci ic ask. Fo example:
3. MNLI (Mul i-Gen e Na u al Language
In e ence): The inpu is a pai o sen ences
whe e he model has o de e mine i he
second sen ence logically ollows om he
i s .
4. NER (Named-En i y Recogni ion): The
inpu is a sen ence, and he ou pu is a
label o each wo d (e.g., "Pe son,"
"Loca ion," "O ganiza ion").
5. SQuAD (S an o d Ques ion Answe ing
Da ase ): The inpu is a pai o a ques ion
and a pa ag aph. The model's ask is o
iden i y he span (s a and end posi ion)
o he answe wi hin he pa ag aph.
6. T aining: Only a small po ion o he
model, p ima ily he new ou pu laye , is
ained. The co e BERT laye s a e sligh ly
adjus ed du ing his p ocess. This ine-
uning adap s he model's p e- ained
knowledge o he speci ic nuances o he
new ask.
Resul s and Discussions
Se o expe imen s e alua ed he
pe o mance o he Uni ied NLP Toolki on a
ex classi ica ion ask, speci ically sen imen
analysis. We compa ed he oolki 's
pe o mance agains wo baselines: a
Monolingual Baseline (a sepa a e IndicBERT
model ine- uned o each indi idual language)
and a Nai e Baseline (a simple TF-IDF model
wi h a linea classi ie ).
Table 1. Tex Classi ica ion pe o mance(F1-Sco e)
Language
Uni ied NLP Toolki
Monolingual Baseline
(IndicBERT)
Nai e Baseline
(TF-IDF)
Hindi
91.2%
90.8%
78.5%
Ma a hi
87.5%
86.9%
75.1%
Tamil
82.4%
78.3%
68.2%
Telugu
83.1%
79.2%
69.5%
A e age
86.1%
83.8%
72.8%
The able clea ly shows ha he
Uni ied NLP Toolki consis en ly ou pe o ms
bo h baseline models ac oss all ou languages.
Fo high- esou ce languages like Hindi and
Ma a hi, he pe o mance gap be ween he
Uni ied Toolki and he Monolingual Baseline
is small, indica ing ha he uni ied model does
no comp omise pe o mance o hese
es ablished languages.
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D . Jyo i R. Jadha
301
The mos signi ican pe o mance gain
is obse ed o he low- esou ce D a idian
languages, Tamil and Telugu. The Uni ied
Toolki shows a no able F1-sco e inc ease o
4.1% and 3.9%, espec i ely, o e hei
monolingual coun e pa s. This s ongly
sugges s ha he oolki is success ully
le e aging c oss-lingual knowledge o boos
pe o mance whe e i 's needed mos .
Table 2. Named En i y Classi ica ion pe o mance (F1-Sco e)
Language
Uni ied NLP Toolki
Monolingual Baseline
(IndicBERT)
Nai e Baseline
(TF-IDF)
Hindi
88.5%
87.9%
65.2%
Ma a hi
85.3%
84.1%
60.1%
Tamil
79.8%
72.5%
55.4%
Telugu
80.5%
73.1%
56.8%
A e age
83.5%
79.4%
59.4%
The esul s o he NER ask a e e en
mo e p onounced. The Uni ied Toolki 's
a e age F1-sco e is 4.1% highe han he
Monolingual Baseline.The di e ence is
pa icula ly s iking o Tamil and Telugu,
whe e he uni ied model achie es a subs an ial
pe o mance inc ease o 7.3% and 7.4%,
espec i ely. This p o ides s ong e idence
ha he c oss-lingual embeddings and sha ed
ep esen a ion lea ned by he oolki a e highly
e ec i e o low- esou ce NER.The wide gap
be ween he deep lea ning models and he
Nai e Baseline (a adi ional Condi ional
Random Field model) highligh s he supe io
pe o mance o ans o me -based
a chi ec u es o his ask.
Indian Mul ilingual P ocessing
Na u al Language P ocessing (NLP) is
a key d i e o p og ess ac oss a ious sec o s
in India, p omo ing bo h inclusi i y and
e iciency. By enabling echnologies o
unde s and and p ocess egional languages,
NLP signi ican ly enhances use engagemen
h ough ea u es like con e sa ional cha bo s,
oice assis an s, and mo e accu a e sea ch
engines, which ca e o a wide local audience.
This p og ess also leads o imp o ed
accessibili y, as oice-ac i a ed sys ems and
ex - o-speech echnologies empowe
indi iduals wi h disabili ies o limi ed li e acy,
while also democ a izing access o c ucial
in o ma ion, such as legal documen s, in hei
na i e ongues. Economically, NLP is a
ca alys o g ow h by in eg a ing egional
languages in o co e sec o s like ag icul u e,
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D . Jyo i R. Jadha
302
banking, and e-comme ce. Fu he mo e, i
plays a i al ole in cul u al and educa ional
p ese a ion by aiding in he digi iza ion o
adi ional manusc ip s and li e a y wo ks, and
by enabling he c ea ion o in e ac i e
educa ional pla o ms and c ea i e s o y elling
applica ions in India's e nacula languages.
Conclusion:
The key pa o he IndicNLPSui e, a e
i s ained on IndicCo p, which s ands as he
la ges publicly a ailable collec ion o Indian
language ex s. Wi h an a e age size nine
imes g ea e han OSCAR, he p e ious
la ges co pus, IndicCo p p o ides an
unp eceden ed amoun o da a o ou aining
p ocess. A e aining, we igo ously e alua e
ou models using he IndicGLUE benchma k
o measu e hei pe o mance ac oss a ious
asks. We' e p oud o epo ha ou models,
including IndicBERT and IndicFT, ha e
shown p omising esul s. Despi e being
signi ican ly smalle han o he la ge-scale
models, IndicBERT o en deli e s
compa able, and in some cases, e en supe io
pe o mance. While hese ea ly esul s a e
encou aging, we acknowledge ha he e's s ill
ample oppo uni y o u he imp o emen
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