Putting languages into perspective: A comprehensive database of English words and their Croatian equivalents

Abstract

Numerous studies have addressed the issue of English words in the context of their adaptation, but there still exists the need for a systematic perspective on English words in terms of their number and frequency of appearance. This article will outline the procedure behind the compilation process of unadapted English words in the Croatian language with a comprehensive description of the final product – an open-access database of single- (SWE) and multi-word (MWE) English expressions extracted from Croatian web corpora (ENGRI and hrWaC) by means of computational-linguistic tools and manual extraction. The final version of the database contains 2,982 English words in their unadapted form (e.g. blockbuster), and 18 words which appear with English orthographic properties in combination with Croatian inflectional affixes (e.g. downloadati). Each SWE and MWE entry in the database is accompanied with frequencies of appearance in both corpora as well as its Croatian equivalent where available (29.58% of all entries are listed without an equivalent). The database serves as the first systematic representation of English words in Croatian and provides an indispensable tool for further research into the phenomenon while at the same time opening the door to a new line of research – cognitive processing of English words in Croatian.

Full text

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JASMINA JELČIĆ ČOLAKOVAC ¹ DOI: 10.15290/CR.2024.45.2.04
University of Rijeka, Faculty of Maritime Studies, Croatia
https://orcid.org/0000-0002-1241-1283
IRENA BOGUNOVIĆ
University of Rijeka, Faculty of Maritime Studies, Croatia
https://orcid.org/0000-0002-2956-7014
Putting languages into perspective:
A comprehensive database of
English words and their Croatian
equivalents
Abstract. Numerous studies have addressed the issue of English words in the context of their adaptation, but
there still exists the need for a systematic perspective on English words in terms of their number and frequen-
cy of appearance. This article will outline the procedure behind the compilation process of unadapted English
words in the Croatian language with a comprehensive description of the final product – an open-access database
of single- (SWE) and multi-word (MWE) English expressions extracted from Croatian web corpora (ENGRI and
hrWaC) by means of computational-linguistic tools and manual extraction. The final version of the database
contains 2,982 English words in their unadapted form (e.g. blockbuster), and 18 words which appear with English
orthographic properties in combination with Croatian inflectional aixes (e.g. downloadati). Each SWE and MWE
entry in the database is accompanied with frequencies of appearance in both corpora as well as its Croatian
equivalent where available (29.58% of all entries are listed without an equivalent). The database serves as the first
systematic representation of English words in Croatian and provides an indispensable tool for further research
into the phenomenon while at the same time opening the door to a new line of research – cognitive processing
of English words in Croatian.
Keywords: English words in Croatian, language borrowing, corpus search, database compilation, anglicisms
1. Introduction
Borrowing from English has been documented in many languages. Words and expressions
borrowed from English have been investigated in Spanish (e.g. Alvarez-Mellado, 2020), Italian
1 Address for correspondence: Faculty of Maritime Studies, Foreign Languages Department, Studentska 2, 51
000 Rijeka, Croatia. E-mail: ja s m in a .jel c i c @ p fr i. u n ir i.h r

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(e.g. Pulcini et al., 2012), Norwegian (e.g. Greenall, 2005), Slovenian (e.g. Čepon, 2017), Czech
and Slovak (e.g. Entlová & Mala, 2020), Japanese (e.g. Kay, 1995) and South Korean (e.g. Rüdiger,
2018), to mention some of them. Croatian has also become highly receptive to borrowing from
English (Mihaljević Djigunović & Geld, 2003). As a result, many English loanwords have become
part of Croatian everyday communication (e.g. Nikolić-Hoyt, 2005). The prestigious status of
a donor language (e.g. Crystal, 2003) reduces the tendencies of borrowed words to fully adapt
to the rules of the recipient language (e.g. McKenzie, 2010; Nikolić-Hoyt, 2005).
Borrowed words are generally described in terms of the degree of their adaptation to the
recipient language (e.g. Görlach, 2002; Entlová & Mala, 2020) or their inclusion in the language
(e.g. Kay, 1995; Međeral, 2016). A distinction is made between words which have adapted, fully
or partially, to the recipient language and those which occur in an original, unadapted form
(e.g. event, freelancer, bodybuilder, etc.). Terminology related to unadapted English loanwords is
not unified, so terms like ‘raw anglicisms’ (e.g. Kavgić, 2013), ‘English loanwords’ (e.g. Görlach,
2002; Kay, 1995; Rüdiger, 2018), ‘foreign words’ (e.g. Međeral, 2016; Muhvić-Dimanovski & Skelin
Horvat, 2006) and ‘pseudoanglicisms’ (e.g. Filipović, 1990) can be found.
This paper focuses on the latter category, i.e. words borrowed from English which retain
the original properties of the donor language, and sometimes take Croatian aixes (e.g. eventi
(m. nom. pl.), freelancerima (m. dat. pl.), bodybuildera (m. gen. sg.), etc.). Such words have not
become an integral part of Croatian and are perceived as foreign by native speakers, so the
term ‘foreign words’ seems appropriate. For the purpose of precision, the term ‘English words’
will be used (e.g. Brdar, 2010; Ćoso & Bogunović, 2017).
Borrowed words have long been a subject of discussion among Croatian linguists (Muh-
vić-Dimanovski & Skelin Horvat, 2006), who generally recommend the use of native words
(e.g. Hudeček & Mihaljević, 2005). There are several ways to deal with borrowed words: using
multi-word expressions and descriptions, using an existing word and giving it a new meaning,
or introducing new words and calques. However, it seems that not all such solutions have been
accepted among Croatian speakers (e.g. Drljača, 2006; Patekar, 2019), especially in domains like
show business and information technology (e.g. Drljača Margić, 2014). Multi-word expressions and
descriptions are oen complex to use (e.g. Drljača, 2006). For example, according to the website
Bolje je hrvatski! (bolje.hr), the English word soware is translated as programska podrška (Eng.
‘program support’), and developer as razvojni inženjer (Eng. ‘development engineer’). The com-
plexity of these solutions is best illustrated by the translation of the syntagm soware developer
as razvojni inženjer programske podrške (Eng. ‘program support development engineer’). Giving
a new meaning to an already existing word can result in insuicient precision (Drljača, 2006), as
in spravica (Eng. ‘small device’) for gadget (bolje.hr). Finally, the process of introducing a new
word or calque is usually slow (e.g. Muhvić-Dimanovski & Skelin Horvat, 2008). For example, the
English word selfie gained worldwide popularity in 2012, while the Croatian equivalent sebić was
proposed in 2014 (Halonja & Hudeček, 2014).

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In Croatia, a vast body of research has investigated the phenomenon of English words using
dierent theoretical approaches and methods (e.g. Ćoso & Bogunović, 2017; Drljača Margić, 2014;
Filipović, 1990; Patekar, 2019). However, most researchers either focus on selectively chosen
English words (e.g. Ćoso & Bogunović, 2017; Patekar, 2019) or rely on small-scale, domain-specific
corpora (e.g. Brdar, 2010; Hudeček & Mihaljević, 2005). What seems to be neglected is a data-driv-
en approach. One possible reason for that could be the fact that the development of Croatian
computational linguistic tools and resources lagged behind those of other languages in the past
(e.g. Tadić et al., 2012). However, this is now changing and some new language technologies have
been developed in the last decade (Tadić, 2022). Aside from traditional dictionaries (e.g. Filipović,
1990; Görlach, 2002), new resources have emerged. For example, the above-mentioned website
Bolje je hrvatski!, developed by the Institute for Croatian language and linguistics, selectively
records the intake of foreign words into Croatian and proposes native equivalents. Borrowed
words, including some English words, can also be found in an online dictionary of neologisms
(Muhvić-Dimanovski et al., 2016). On the other hand, Kontekst.io searches the Croatian web
corpus, hrWaC (Ljubešić & Klubička, 2016) to find a specific word. The results include informa-
tion about the word’s frequency as well as the frequencies of similar words. Word frequency
can also be obtained by searching for a specific word in the available corpora via the platform
Sketch Engine (Kilgarri et al., 2004). The results are presented in context, and various options
are available to filter them out (e.g. English words occurring in English contexts, names, etc.).
However, this method cannot be used to create lists of English words, as the existing corpora
are linguistically processed (e.g. tokenized, lemmatized, morphosyntactically tagged, etc.) ac-
cording to the rules of the Croatian language.
In other languages, researchers have used dierent methods for the extraction of anglicisms
and English words from corpora. Some authors opted for manual search (e.g. Luján García,
2017), and others used the available tools and resources or created new ones (e.g. Alex, 2005;
Andersen, 2012). For example, an unsupervised system, based on the idea that there is a relation
between Google search results and language membership, was developed for the classification
of anglicisms in German (Alex, 2005). Another approach combined lexicon lookup with character
N-grams (e.g. Furiassi & Hofland, 2007). Supervised machine learning methods in combination
with N-grams has also yielded reliable results (e.g. Alvarez-Mellado, 2020; Serigos, 2017), and it
was used to create the Database of English words in Croatian (Bogunović & Kučić, 2022).
The Database of English words in Croatian (Bogunović & Kučić, 2022) contains 9,453 English
words, some of which (e.g. summit, vintage, benefit) originate from other languages. Although
some authors (e.g. Filipović, 1990) state that even words that are not English in origin but were
borrowed from English can be considered English loanwords, establishing each word’s etymology
was not the goal of Bogunović and Kučić’s work. The Database represents the result of algorith-
mic classification and manual evaluation of word lists produced by the algorithm. The results
are publicly available on Figshare.com as an open source of data. However, it does not provide
any information about the availability of Croatian translational equivalents and their frequencies.

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Moreover, the database only lists extracted English words with their frequencies, without further
elaboration of context-related problems such as polysemy, interlingual cognates, proper names, etc.
The following research aims to fill these gaps by further elaborating the Database of English
words in Croatian. The paper presents the results of such an endeavor.
2. Method
The Database of English words and their Croatian equivalents (Bogunović, Jelčić Čolakovac &
Borucinsky, 2022, hereinaer: the Database) presents an elaboration of Bogunović and Kučić’s
(2022) database, based on the ENGRI corpus (Bogunović et al., 2021; Bogunović & Kučić, 2021),
which contains texts from the 12 most popular Croatian news portals between 2014 and 2020.
The Database was further updated with data from both ENGRI and hrWaC 2.2 (Ljubešić & Klu-
bička, 2016), built by crawling the .hr top-level domain in 2011 and again in 2014, using the
SketchEngine (SkE) platform (Kilgarri et al., 2004).
2.1. Manual search and evaluation
Manual evaluation of corpus data was used to eliminate the entries from Bogunović and Kučić’l s
(2022) database which had appeared in the corpora either in embedded English texts or as part
of an English multi-word expression (MWE), the phrase constituents of which the algorithm rec-
ognized as single-word (SWEs) entries. The issue of English words appearing in English contexts
rather than Croatian sentences was ultimately resolved through SkE search and Xf tagger filtration.
Manual search and evaluation was, however, indispensable in resolving the MWE issue, along
with a number of problems which were brought to our attention during corpus search (Jelčić
Čolakovac & Borucinsky, 2023). These issues include:
1. the disambiguation of proper names and common nouns (e.g. PlayStation as a company
vs. playstation as a term for a gamer console, etc.);
2.
the absence of diacritics from Croatian words appearing in web-crawled sources (e.g. Cro.
vaše (pro., 2nd pers. pl.) ‘yours’ vs. Eng. vase ‘decorative container’, etc.);
3. meaning disambiguation (e.g. Cro. gem (m. nom. sg.) ‘term in a tennis scoring match’ vs.
Eng. gem ‘a precious stone’, etc.);
4. inflection of Croatian word classes (e.g. Cro. elaborate (m. acc. pl.) ‘a written elaboration’
vs. Eng. elaborate (v.) ‘to explain’ or Eng. elaborate (adj.) ‘planned in detail’, etc.);
5. false cognates (e.g. Cro. file (m. nom. sg.) ‘chicken breast’ vs. Eng. file ‘document’, etc.)
6. adapted English forms (e.g. Cro. bend (n.) ‘band, a group of musicians’ vs. Eng. ‘bend’ (v.)
‘to turn or force from straight or even to curved or angular’, etc.).
Once the list of English words was filtered using corpus tools and manual search, our eort
was directed towards providing Croatian translational equivalents for each entry in the Database. ²
2 The term ‘entry’ will be used interchangeably throughout the paper to refer to both single-word (SWE) and
multi-word (MWE) expressions from the Database.

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Published sources (dictionaries, books, articles, etc.) on the topic of English loanwords in Croatian
served as the stepping-stone to finding adequate equivalents, and, if these proved insuicient,
web sources and both corpora were used to aid the search.
Table 1 lists examples from the Database and sources which were used to identify their
Croatian equivalents.
Table 1. An exemplification of sources for Croatian equivalents
Entry Croatian equivalent Source
ability sposobnost (f. nom. sg.) Bujas (2019)
aerparty zabava nakon posla (f. nom. sg.; prep.;
m. dat. sg.) Bolje (h t t p s :// b o lj e . h r/ )
bookmark straničnik (m. nom. sg.) Muhvić-Dimanovski and Skelin Horvat
(2008)
knjižna oznaka (f. adj. sg.; f. nom. sg.) Glosbe
(https://hr.glosbe.com/)
dočitnica (f. nom. sg.) Wiktionary
(ht tp s://w w w.w ikt io na ry.org /)
composite presjek (m. nom. sg.) hrWac
kompozitan (m. adj. sg.) hrWac
dongle hardverski ključ (m. adj. sg.; m. nom.
sg.) Glosbe (https://hr.glosbe.com/)
grooming uređivanje pasa (n. nom. sg.; m. gen.
pl.) Bolje (h t t p s :// b o lj e .h r/ )
homepage početna stranica (f. adj. sg.; f. nom. sg.) Bujas (2019)
službena stranica (f. adj. sg.; f. nom. sg.) Glosbe (https://hr.glosbe.com/)
blind tasting kušanje na slijepo (n. nom. sg.; prep.;
n. adv. sg.) hrWac
acting coach učitelj glume (m. nom. sg.; f. gen. sg.) Bujas (2019)
učiteljica glume (f. nom. sg.; f. gen. sg.)
corkage fee naknada za služenje (f. nom. sg.; prep.;
n. nom. sg.) ENGRI
čeparina (f. nom. sg.) Glosbe (https://hr.glosbe.com/)
remote reality udaljena stvarnost (f. nom. sg.) ENGRI

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2.2. Semantic analysis of sentential context
To evaluate the context in which a particular English word is used in Croatian, the two corpora
had to be swept for representative samples of sentential context using the SkE search tools. For
those entries appearing in multiple contexts, the predominant context was used in assigning
the word to a specific area of human activity, i.e. the semantic field it is usually associated with
in the Croatian corpora. The word design (RF = 7.1402) is one such example, which also appears
in contexts related to information and communications technology (ICT), but is predominantly
used in contexts related to the fashion industry. Other examples include English words such
as abuse (RF = 0.2683; appearing in law and politics-related context, but predominantly in
ICT-related context) and combat (RF = 0.5191; appearing in sentential contexts related to war,
but predominantly in sport and gaming contexts). For those words appearing across multiple
contexts with similar frequencies, or whose semantic field could not be determined due to the
word’s generic reference, the ‘OTHER’ category was introduced. Such instances include SWEs
like review (RF = 0.7497), progress (RF = 0.5623), position (RF = 1.5416) and reach (RF = 0.4572),
and MWEs like against type (RF = 0.0095), boom eect (RF= 0.0019) and extreme ways (RF=
0.0087), which appear in the corpora in various contexts. Based on in-depth semantic analysis
of sentential context in which the English words appeared in the Croatian corpora, 12 semantic
categories and 12 subcategories have been introduced (Table 2).
Table 2. Representation of semantic categories (n = 12) and subcategories (n =12) for English words in
the Database
Category Description Examples
(1) ANT relating to animals, plants, and non-hu-
man entities with human traits beast, spider, queen bee
(2) ART
relating to entertainment and show busi-
ness, and branches of human creative acti-
vities, such as music, dance, literature, etc.
classic, comeback, casting
director
Subcategory
MUSIC relating to the music industry airplay, orchestra, jam ses-
sion
TV relating to tv, news, and film industry binge, spoiler, body horror
(3) PEOPLE relating to people, human behavior and
activity, and social phenomena in general
gay, teenager, attention
whoring
Subcategory
LANG relating to language and linguistic pheno-
mena, metaphor, and idiomatic language actually, anyway, be on fire

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Category Description Examples
WAR relating to combat and human conflict in
general battle, raid, ground zero
LP relating to government, law, and politics e-government, council, cell
block
(4) BUSINESS relating to business and economy, finance,
money, and the world of work in general
brownfield, oset, debt
equity swap
Subcategory
COMMERCE
relating to the act of buying and/or selling,
product advertising, and consumerism in
general
delivery, tester, customer
loyalty
(5) TECH relating to technology and operation of
machinery
clutch, joystick, driver
screen
Subcategory
ICT
relating to information and communica-
tions technology, Internet, and computer
science
feed, inbox, big data
TRANS-
PORT
relating to means of transport and trans-
port-connected activities
cargo, landing, economy
class
(6) SCIENCE relating to science and scientific activity molecular, nuclear, case
study
Subcategory
EDUCATION relating to educational activities academy, e-learning, action
learning
(7) FASHION relating to clothing, make-up, style, and
the beauty business casual, styling, dress code
(8) FOOD relating to food and drink, and the act of
dining and diet in general beef, drive-in, blind tasting
(9) HEALTH relating to health, medicine, and the hu-
man body operation, pill, blood aging
Subcategory
SPORT relating to sport and games dra, playmaker, alpine
skiing
(10) TOURISM relating to the tourist business and travel
for pleasure
all-inclusive, booking, foot
holiday
Subcategory
NATURE relating to environment and ecology emission, winter, hot spring

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Category Description Examples
LOC relating to specific places and localities penthouse, room, food cor-
ner
(11) QUANTITY relating to quantity, size, position or du-
ration low-level, zero, long term
(12) OTHER words with generic references and/or wor-
ds appearing across multiple contexts
ancient, progress, free cho-
ice
The proposed categorization is based on the Croatian contexts in which the English words
appear, and can by no means be taken to reflect the semantic contexts in which these words are
regularly used in English. We would also like to stress that only the most representative seman-
tic categories have been identified. Furthermore, subcategories have been assigned based on
available corpus evidence and where repetitive overlap between semantic categories has been
observed (e.g. NATURE and LOC have been categorized under TOURISM since a considerable
number of words belonging to the two subcategories have repeatedly appeared in contexts re-
lating to tourism and travel, albeit with lower frequencies than in their assigned subcategories).
Finally, aer resolving problems through manual search and human evaluation, finding trans-
lational equivalents in Croatian, and assigning semantic categories to each entry, the Database
has been published as an open-source linguistic resource, with the representation of data in
tabular form (row per entry) (Figure 1).
Figure 1. The Database available as open source on Figshare.com
3. Results and discussion
The Database contains 2,964 English words and expressions which appear in Croatian texts in
their original, unadapted form (e.g. blockbuster, cyberbullying, shopping, zombie, skin, etc.) and
18 words with English orthographic properties in combination with Croatian inflectional aixes
(e.g. downloadati (v.t., inf.) ‘to download’, managerica (f. nom. sg.) ‘female manager’, etc.).

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3.1. Word frequencies
Each database entry is accompanied with a Croatian equivalent if the latter exists in the Cro-
atian language. Absolute frequencies expressing the total number of corpus occurrences for
each entry in the database and relative frequencies expressing the proportion of each entry’s
occurrence in the entire corpus (absolute frequency divided by the total number of words per
corpus) are listed for both the English expression and, if applicable, its equivalent. ENGRI and
hrWaC 2.2 corpora served as the starting point for the calculation of frequencies which are
represented in the Database both per corpus and combined: ENGRI absolute frequency (Eaf),
ENGRI relative frequency (Erf), hrWaC absolute frequency (Haf), and hrWaC relative frequency
(Hrf). The Database also provides data on combined relative frequencies (RF) for both corpora.
Only five entries have been shown to appear in the corpora more than 100,000 times (web,
real, blog, show, and post), while 85 words (2.85%) appear more than 10,000, and less than
100,000 times. SWEs belonging to this frequency band include link, fan, e-mail, online, net, mail,
rock, jazz, etc., with only one MWE appearing in the corpora more than 10,000 times (big brother)
(cf. Table 2). In total, 709 SWEs and 27 MWEs appear between 1,000 and 10,000 times, which
accounts for 24.68% of the Database. If we take the bottom-up perspective on frequencies,
41.78% of all Database entries are recorded 100 times or less in the corpora (184 SWEs and 1062
MWEs respectively), with some MWE entries (e.g. age verification, all girl band, anti age eect,
anti stain eect, appearance fee (RF = 0.0012), etc.) and only five SWE entries (mapmatching,
mastershot, spraypainting (RF= 0.0012), and personalization (RF = 0.0007)) appearing only once
in the Croatian context. ³
Table 3 illustrates the 10 entries with the highest combined relative frequencies (RF) in the
Database.
Table 3. Database entries with the highest relative frequencies on the SWE and MWE lists
SWEs
Entry Eaf Erf Haf Hrf RF
real 86346 99.3957 46730 33.4321 132.8278
web 27648 31.8265 116672 83.4708 115.2973
show 69705 80.2397 36043 25.7863 106.0260
blog 12710 14.6309 112350 80.3787 95.0096
post 18565 21.3708 85431 61.1200 82.4908
3 We would like to note here that all Database entries reflect the spelling of the word(s) as it was used in the
Croatian context, which does not necessarily adhere to the standards of the spelling rules for the English
language (e.g. anti age eect instead of anti-age eect, etc.). The same approach was followed in sorting the
entries into SWEs and MWEs (e.g. mapmatching rather than map matching, etc.).

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SWEs
fan 40273 46.3596 39346 28.1494 74.5089
link 6037 6.9494 79778 57.0757 64.0251
online 25053 28.8393 43498 31.1198 59.9592
e-mail 19318 22.2376 49698 35.5555 57.7931
mail 15473 17.8115 42794 30.6162 48.4277
MWEs
big brother 9657 11.1165 4833 3.4577 14. 5742
stand(-)up 2161 2.4876 2055 1.4702 3.9578
fast food 1755 2.0202 2532 1.8115 3.8317
triple(-)double 2920 3.3613 398 0.2847 3.6460
fair play 1606 1.8487 2036 1.4566 3.3053
single 815 0.9382 2329 1.6662 2.6044
made in 663 0.7632 2510 1.7957 2.5589
red carpet 285 0.3281 3061 2.1899 2.5180
open source 141 0.1623 2949 2.1098 2.2721
must have 828 0.9531 1596 1.1418 2.0950
3.2. Single-word and multi-word expressions
The categorization of English words into 1,728 single-word (Cro. jednorječne) (SWEs) and 1,254
multi-word (Cro. višerječne) expressions (MWEs) represents one of the two major elaborations
of Bogunović and Kučić’s (2022) database. The restrictions of the original algorithm (Bogunović
& Kučić, under review), which produced word lists for both databases, prevented it from recog-
nizing English MWEs in the web-crawled sources, hence turning manual evaluation and corpus
search into necessary methodological steps in the compilation of our Database.
On the one hand, a detailed manual search of both hrWaC and ENGRI corpora revealed that
many of the words which were initially tagged by the algorithm as SWEs were, in fact, part of
an English MWE used in a Croatian context (such examples include English words like flower
(appearing only as a constituent in the MWEs flower power and flower fashion) or cat (appearing
only in MWEs cat and mouse, cat person, and cat people). On the other hand, further examination
of corpus examples indicated that some English words were used in Croatian as either a SWE
or part of an MWE. These words include, for example, age (appearing also in MWEs age verifica-
tion, anti-age (eect), and coming of age), horror (also in body horror and shock horror), or zero

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(also in ground zero, patient zero, size zero models, zero companies, zero hour contract, and zero
waste). These entries used as both SWEs and part of English MWEs in Croatian needed to be
taken into consideration when absolute and relative frequencies were concerned; it was upon
the evaluators to rely on KWIC (key word in context) searches in order to distinguish between
the SWE and MWE frequencies for words appearing in both wordlists (e.g. the occurrences of the
word coee in the MWEs coee culture and ice coee needed to be subtracted from the overall
frequencies for the SWE coee). Once these issues had been resolved, the absolute and relative
frequencies could be added to the Database for both SWEs and MWEs.
Compounds presented a particular challenge in the process of compilation since some items
appeared in the Croatian texts in both hyphenated and non-hyphenated forms. If the English
expression appeared in the corpora either as a single word or a hyphenated compound (e.g.
blu(e)-ray, all-in-one, talk-show, co-creation, all-inclusive, mid-range, one-on-one, speech-to-text,
co-production, follow-up, drive-in, pet-friendly, ready-made, etc.), it was categorized as a SWE.
The MWE entries used in Croatian as hyphenated MWEs (e.g. make(-)up artist, e(-)book reader,
regional stand(-)up, pop(-)up corner, etc.) were categorized under MWEs with the hyphen placed
in parentheses in order to indicate its optionality. Finally, six entries were listed under both
SWEs and MWEs since they appeared in the corpora with and without a hyphen, i.e. as a MWE
(triple(-)double, hi(-)tech, jet(-)ski, head(-)up, cut(-)out, and stand(-)up). The final categorization
yielded 62 hyphenated compounds on the SWE list, which constitutes 3.59% of the total number
of single-word entries in the Database whereas the MWE list included 13 hyphenated entries
(1.04% of the total number of multi-word entries). The SWE compound which most frequently
appeared in the Croatian corpora is e-mail, with a combined relative frequency of 57.79, followed
by start-up (RF = 11.43), triple(-)double (RF = 3.89), and blu(e)-ray (RF = 3.07).
3.3. English words with Croatian aixes
Apart from the inclusion of unadapted English words, the Database also lists English words
which have taken on Croatian inflectional forms (0.60% of the total number of database entries,
18 entries in total), the majority of which are single-word entries (two inflected MWE entries
have been recorded, namely location manager(ica) (managerica, f. nom. sg.) ‘female location
manager’ and teen seks comedy (seks, m. nom. sg.) ‘teen sex comedy’.
The largest portion of inflected words have taken on the Croatian inflectional suix -ica,
which denotes the female gender in Croatian and indicates the female doer of an activity (ex-
amples include: Cro. sprinterica (f. nom. sg.) ‘a female sprinter’; Cro. managerica (f. nom. sg.)
‘a female manager’; Cro. youtuberica (f. nom. sg.) ‘a female youtuber’; Cro. swingerica (f. nom.
sg.) ‘a female swinger’, etc.). The inflectional suix was also recorded with wagsica (f. nom.
sg.), even though the word in English may only refer to women (WAG is literally the acronym
of ‘wife and girlfriend’ and, according to the Cambridge Dictionary, stands to denote ‘a wife or
girlfriend, especially of a well-known sports player’). The -ica suix also appeared in hoodica
(f. nom. sg. ‘a hoodie’), where it does not refer to a female doer, but rather the female gender

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of the noun in question, whereby feminine noun properties were added to the English word
hoodie, probably due to meaning similarities with another Croatian word, majica (f. nom. sg.,
‘any type of T-shirt, blouse, or shirt’).
Other Croatian inflectional suixes which appeared with English words in the corpora include
the nominal suix -anje (swinganje (n. nom. sg.) ‘the act of swinging’), the Croatian verbal suix
-(a)ti (downloadati (v.t., inf.) ‘to download’; googlati (v.t., inf.) ‘to google’) and the adjectival/ad-
verbial suix -no (maximalno (n. nom. sg.) ‘in the largest or greatest manner’). If an English word
was used in the Croatian context in both its unadapted and inflectional form, the two words
were listed as separate entries (such was the case with download and downloadati).
3.4. Croatian equivalents
The second major elaboration of Bogunović and Kučić’s (2022) database lies in the addition
of Croatian equivalents and their absolute and relative frequencies in both corpora. A total of
29.58% of all the entries in the Database are listed without a Croatian equivalent (296 SWEs and
586 MWEs), while 54 SWE entries and 28 MWE entries are listed with more than one possible
equivalent. More than two Croatian equivalents are listed for 11 SWE entries (bookmark, man-
ager, managerica, maker, kickboxer, investor, hero, hater, stylist, rookie, and policy-maker) and 3
MWE entries (cloud computing, comedy club, and cooking class).
Translational equivalents were found in Croatian for most entries in the Database (e.g. Eng.
ability/Cro. sposobnost, Eng. air guitar/Cro. zračna gitara, Eng. zombie/Cro. zombi, Eng. wild/Cro.
divlji, Eng. winner/Cro. pobjednik, Eng. city/Cro. grad). In those instances where the English word
appeared in the Croatian context bearing more than one meaning, Croatian equivalents were
listed separately to account for each of the word’s meanings (e.g. Eng. company/ Cro. kompanija
‘an organization that sells goods or services in order to make money’, društvo ‘the fact of being
with a person or people, or the person or people you are with’). Croatian equivalents for other
meanings of company which are found in English are not listed in the Database since the word
does not appear to be used in those senses (e.g. Eng. company ‘a group of actors, singers, or
dancers who perform together’, ‘a large group of soldiers’, ‘an organized group of young women
who are guides’, etc.). Similarly, a word was provided with the Croatian equivalent which would
belong to the word category in which it was used in the Croatian context. This is to say, English
words such as update/Cro. posuvremeniti (v.t., inf.), edit/Cro. urediti (v.t., inf.), ski/Cro. skijati
(v.int., inf.), or record/Cro. snimiti (v.t., inf.) were provided with the translation which reflected
its verbal use in Croatian (all of the listed examples are used in Croatian texts as verbs, never
as nouns). There are also instances of entries in the Database for which English loanwords in
Croatian are listed as translational equivalents due to their high frequency of use among Croa-
tian speakers. In total, 186 database entries (6.24%) are accompanied by a translational equiv-
alent in Croatian that is an English loanword in origin. If we are to analyze each of the two lists
separately, SWEs (150 entries, 8.68% of all SWEs) are more frequently accompanied by English
loanwords than MWEs (36 entries, 2.87%) in our Database. English loanwords usually appear

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in relation to SWEs denoting a doer of an action (Eng. babysitter/Cro. bejbisiter, bejbisiterica,
Eng. blogger/Cro. bloger, blogerica, Eng. breaker/Cro. brejker, brejkerica, Eng. hater/Cro. hejter,
hejterica, Eng. leader/Cro. lider, liderica, etc.). As expected, English loanwords also frequently
appear among English words from the domains of commerce, economy and business (e.g.
Eng. banner/Cro. baner, Eng. bestseller/Cro. bestseler, Eng. budget/Cro. budžet, Eng. consulting/
Cro. konzalting), popular culture (e.g. Eng. blockbuster/Cro. blokbaster, Eng. fake/Cro. fejk, Eng.
fancy/Cro. fensi), sports (e.g. Eng. bridge/Cro. bridž, Eng. fitness/Cro. fitness, Eng. jogging/Cro.
džoging) and ICT (e.g. Eng. cluster/Cro. klaster, Eng. disc/Cro. disk, Eng. inch/Cro. inč, Eng. scart/
Cro. skart). The results in the case of MWEs revealed that out of 36 English expressions only 7
were accompanied by an English loanword as a translational equivalent (e.g. Eng. spin doctor/
Cro. spin doktor, Eng. shock horror/Cro. šok horor), whereas in the case of the other 29 MWEs
only one phrasal constituent was a loanword from English. Such examples include Eng. gay
friend/Cro. gej prijatelj, Eng. gala opening/Cro. gala otvorenje, Eng. ultra clear/Cro. ultra čist, or
Eng. travel blog/Cro. putopisni blog.
Multiple Croatian equivalents were oentimes available for one and the same meaning (e.g.
bookmark or corkage fee), in which cases all Croatian equivalents were listed along with their
respective frequencies. Due to the inflectional nature of the Croatian language, English words
referring to people were listed with separate Croatian equivalents where one would denote the
male, and the other the female doer (e.g. Eng. advisor/ Cro. savjetnik (m. nom. sg.), savjetnica
(f. nom. sg.); Eng. publisher/ Cro. izdavač (m. nom. sg.), izdavačica (f. nom. sg.); Eng. rookie/ Cro.
početnik (n. nom. sg.), početnica (f. nom. sg.), novak (n. nom. sg.), novakinja (f. nom. sg.); etc.).
The SWE list includes 65 such entries where both the male and female doer were listed under
Croatian equivalents; this figure does not include rapper/rapperica, manager/managerica, teen-
ager/teenagerica, roller/rollerica, rocker/rockerica, and youtuber/youtuberica, which are listed as
separate database entries since the expressions denoting female doers in Croatian (rapperica,
managerica, etc.) have been adapted to the Croatian language on the morphological level by
the addition of the Croatian inflectional suix, but have retained English orthographic proper-
ties. The MWE list includes 37 such entries where both male and female doers are provided as
equivalents (e.g. Eng. decision maker/Cro. donositelj odluka (m. nom. sg.; f. gen. pl.), donositeljica
odluka (f. nom. sg.; f. gen. pl.); Eng. dirty cop/Cro. korumpirani policajac (m. adj. sg.; m. nom.
sg.), korumpirana policajka (f. adj. sg.; f. nom. sg.); Eng. gay friend/Cro. gej prijatelj (m. adj. sg.;
m. nom. sg.), gej prijateljica (f. adj. sg.; f. nom. sg.); Eng. patient zero/Cro. nulti pacijent (m. adj.
sg.; m. nom. sg.), nulta pacijentica (f. adj. sg.; f. nom. sg.); etc.). In total, Croatian equivalents for
male and female doers are listed separately for 102 entries, which comprises 3.19% of the total
number of database entries.
3.5. Semantic categorization
An overview of the database entries from the semantic perspective revealed interesting results
in terms of the areas of human activity they originate from, i.e. the specific context in which

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they usually appear in the Croatian language. The total count of SWEs and MWEs assigned to
each of the 12 categories is listed in Table 4.
Table 4. Representation of the total counts (n) and percentages (%) of SWEs and MWEs across the 12
semantic categories
Category SWEs MWEs Total
n%n% %
PEOPLE 273 15.79 343 27.35 20.66
TECH 351 20.31 131 10.45 16.16
OTHER 291 16.84 141 11.24 14.49
BUSINESS 148 8.56 175 13.96 10.83
HEALTH 165 9.49 158 12.59 10.79
ART 190 10.99 108 8.61 9.99
TOURISM 89 5.15 78 6.22 5.60
FASHION 68 0.04 36 2.87 3.49
FOOD 58 3.36 37 2.95 3.19
SCIENCE 42 2.43 26 2.07 2.28
QUANTITY 41 2.37 16 1.28 1.91
ANT 13 0.75 50.39 0.60
Dierences between single- and multi-word English expressions have also been observed
for the 12 subcategories. The most frequent subcategories on the SWE list were ICT (n = 284,
16.44%), SPORT (n = 126, 7.29%), and MUSIC (n = 82, 4.75%), followed by LANG (n = 48, 2.78%),
TV (n = 41, 2.37%) and COMMERCE (n = 39, 2.26%). On the other hand, SPORT (n = 104, 8.29%),
LANG (n = 101, 8.05%), and ICT (n = 70, 5.58%) were found to be the most frequent subcatego-
ries on the MWE list, followed by COMMERCE (n = 38, 3.03%), TV (n = 35, 2.79%), and LOC (n =
32, 2.55%). In total, the most frequent subcategory in the Database was ICT (N = 354, 11.87%),
followed by SPORT (N = 230, 7.71%) and LANG (N = 149, 4.99%).
The highest percentage of database entries was found to belong to the PEOPLE catego-
ry (20.66%), i.e. they were related to human behaviour and social activity, as well as social
phenomena in general. Some of the examples of database entries assigned to this category
include: words and expressions related to specific people or groups (e.g. youtuber (RF = 2.9318)
and youtuberica (RF = 0.4364), millennials (RF = 0.0544), hooligan (RF = 0.0970), homeless people
(RF = 0.0052), etc.); words related to human (social) activity (e.g. crowdfunding (RF = 2.1826),

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bullying (RF = 1.0180), mobbing (RF = 2.9945), dating (RF = 0.4373), etc.), and words related to
social phenomena, e.g. activism surrounding human sexuality rights (e.g. straight (RF = 0.5610),
gay (RF = 24.0839), queer (RF = 2.8626), drag queen (RF = 0.2211), etc.). These results could be
related to the role of the Internet in today’s society, where English is the dominant language.
Social networking and the Internet in general have been recognized as activities that facilitate
spontaneous vocabulary acquisition (e.g. Godwin-Jones, 2019; Zourou, 2012).
The TECH category was the second most frequently recorded category in the Database
(16.16%). The recorded results are not surprising if we consider that the inflow of English words
in the last few decades closely follows the growth of the ICT industry, namely the Internet. SWEs
like page (RF = 3.5898; Cro. stranica, f. nom. sg.), memory (RF = 1.9727; Cro. memorija, f. nom.
sg.), and domain (RF = 0.3248; Cro. domena, f. nom. sg.) are used in Croatian contexts only in
reference to ICT, and never to refer to their generic denotations (this is why the Croatian word
memorija (as in ‘computer memory’) was used as the translational equivalent for memory, and
not sjećanje (‘a memory or the act of remembering’, n. nom. sg.), which in Croatian can never
be used to refer to the ability of a machine to memorize information, but only to the human
capacity to remember). The influence of ICT is also evident in terms of borrowed multi-word
units, with English MWEs such as big data (RF = 0.5395), cloud computing (RF = 0.4135), and flat
rate (RF = 0.5384) frequently appearing in the Croatian corpora. One possible reason for the
frequent use of ICT-related English words could be positive attitudes towards English words,
especially in this domain (e.g. Drljača Margić, 2014).
3.6. Per-corpus analysis
A per-corpus analysis of database entries revealed significant variations in frequencies collected
for some entries. Table 5 shows the 10 SWE and MWE entries with the highest relative frequen-
cies (rf) in each corpus.
Table 5. Database entries with the highest per-corpus frequencies on the SWE and MWE lists
ENGRI
SWE Entry Eaf Erf MWE Entry Eaf Erf
real 86346 99.3957 big brother 9657 11.1165
show 69705 80.2397 triple(-)double 2920 3.3613
fan 40273 46.3596 stand(-)up 2161 2.4876
summit 28575 32.8936 fast food 1755 2.0202
web 27648 31.8265 fair play 1606 1.8487
online 25053 28.8393 plus size 1531 1.7624
rock 20308 23.3772 open air 929 1.0694
jazz 20269 23.3323 must have 828 0.9531

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ENGRI
e-mail 19318 22.2376 rock and roll 820 0.9439
post 18565 21.3708 street food 800 0.9209
hrWac
SWE Entry Haf Hrf MWE Entry Haf Hrf
web 116672 83.4708 big brother 4833 3.4577
blog 112350 80.3787 red carpet 3061 2.1899
post 85431 61.1200 open source 2949 2.1098
link 79778 57.0757 fast food 2532 1.8115
net 57462 41.1101 made in 2510 1.7957
e-mail 49698 35.5555 black carpet 2466 1.7643
real 46730 33.4321 o topic 2129 1.5232
online 43498 31.1198 stand(-)up 2055 1.4702
mail 42794 30.6162 fair play 2036 1.4566
fan 39346 28.1494 must have 1596 1.1418
With further comparison of the two corpora it has been established that some entries
appeared in one corpus and never in the other. With regard to SWEs, 28 of them appeared in
hrWaC and never in ENGRI, whereas 14 SWEs were found in ENGRI that never appeared in KWIC
searches in hrWaC. Words like generally (Hrf= 0.47), chain (Hrf= 0.37), and screencast (Hrf= 0.09)
were never found in the ENGRI corpus, despite the word generally, for example, appearing 662
times in hrWaC. Similarly, selfie (Erf= 7.08) and blockchain (Erf= 1.24) appeared more than 1,000
times in ENGRI, but were never found in hrWac. Similar results were obtained for MWEs in our
Database, with 64 of them never appearing in ENGRI (e.g. mind map (Hrf= 0.01), girls’ night out
(Hrf= 0.01), critical art (Hrf= 0.01), etc.), and 319 MWEs found in ENGRI, but never in hrWaC (e.g.
ticket point (Erf= 0.25), ice bucket (Erf= 0.15), etc.). These dierences may reflect the dierences
between the two corpora: while ENGRI contains texts collected exclusively from news portals,
hrWaC also includes texts from blogs, forums, etc. Another possible explanation could be the
time period in which the texts were collected. Some words, like selfie, could have become more
popular aer the hrWaC corpus had been compiled.
3. Concluding remarks
The focus of research on borrowed words in Croatian has primarily been on loanwords which
have undergone adaptation to the recipient language. However, the research outlined in this
paper highlights the significance of unadapted English words which can also be found in the
Croatian language. Their number and frequency of occurrence in the Croatian corpora suggest
they have transcended the boundaries of a simple linguistic phenomenon; a considerable number
of English words continue to appear in use despite the fact that acceptable Croatian equivalents

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are readily available to language users. This can be taken as evidence corroborating the pres-
tigious status of English among speakers of other languages, as well as proof of its overarching
influence on all domains of human activity, especially ICT and popular culture.
The Database, in its current size and scope, presents a valuable addition to language resourc
-
es in view of open-science policy. Both the Database and the ENGRI corpus, created primarily
for the purposes of database compilation, are freely available as tools for other researchers
whose topics of interest include, but are not limited to, language contact, borrowing process,
language prestige, corpus linguistics, or even cognitive processing of foreign words in a recipient
language. It serves as a unique tool for the Croatian language, oering a systematic represen-
tation of unadapted English words, while also providing insight into the frequency of their use
among Croatian language speakers. Furthermore, the model of data representation in the Da-
tabase provides a foundation for all types of contrastive linguistic research on borrowed lexis,
where various factors such as word length, type, or frequency are in focus. Since our data are
time-sensitive in nature, our intention is to repeat the compilation process and gather data from
texts published aer 2020, which would allow us to conduct diachronic studies into the status
of English words in Croatian.
ACKNOWLEDGEMENTS
The study outlined in this paper has been supported in part by the Croatian Science Foundation
(HRZZ) under project number UIP-2019-04-1576.
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***
Jasmina Jelčić Čolakovac received her MA degree in English language and History in 2011 at
the Faculty of Arts and Sciences in Rijeka. She obtained her PhD degree in Applied Linguistics in
2017 at the University of Ljubljana. Her research interests include English loanwords in Croatian
and the processing of metaphoric expressions in bilingual speakers. She has been part of the
research team in the newly established Laboratory for Language, Cognition & Neuroscience
(LaconLab) since 2020.
Irena Bogunović received her MA degree in English and Croatian languages in 2008 at the Fac-
ulty of Arts and Sciences in Rijeka. She obtained her PhD degree in Cognitive Sciences in 2017
at the University of Zagreb. Her research interests include English loanwords in Croatian and
their neurocognitive processing by bilingual speakers. She has been acting as the head of the
newly established Laboratory for Language, Cognition & Neuroscience (LaconLab) since 2020.