=Paper=
{{Paper
|id=Vol-3171/paper52
|storemode=property
|title=Quantitative Parameters of J. London's Short Stories Collection “Children of the Frost” and its Translation
|pdfUrl=https://ceur-ws.org/Vol-3171/paper52.pdf
|volume=Vol-3171
|authors=Mariia Bekhta-Hamanchuk,Halyna Oleksiv,Tetiana Shestakevych,Yuliia Shyika
|dblpUrl=https://dblp.org/rec/conf/colins/Bekhta-Hamanchuk22
}}
==Quantitative Parameters of J. London's Short Stories Collection “Children of the Frost” and its Translation==
https://ceur-ws.org/Vol-3171/paper52.pdf
Quantitative Parameters of J. London's Short Stories Collection
“Children of the Frost” and its Translation
Mariia Bekhta-Hamanchuk1, Halyna Oleksiv2, Tetiana Shestakevych3 and Yuliia Shyika4
1,2,3,4
Lviv Polytechnic National University, Stepana Bandery Street, 12, Lviv, 79000, Ukraine
Abstract
The paper presents the quantitative comparative analysis of Jack London’s collection of short
stories “Children of the Frost” and Ukrainian translations by V. Hladka and K. Koriakina
which has been carried out on the basis of the digital marked corpus of original texts. The
novelty of the research lies in the fact that the above-mentioned literary work has not been
previously studied from the statistical perspective. The theoretical background of the study is
outlined, particularly emphasizing the issues of the corpus, corpus annotation and corpus
linguistics software. The source and target texts have been compared according to the
following coefficients: text volume, number of different word forms, number of sentences,
number of letters, number of content words, number of functional words, hapax legomena
and number of words with a frequency of 10 or more. The most frequently used parts of
speech both in source and target texts are stated. The quantitative indices of the lexical level,
which have been calculated on the basis of the general characteristics of the source and target
texts, have been compared. The reproduction of the nominal character of the source text in
the target text has been analyzed.
Keywords1
Corpus, corpus annotation, corpus linguistics, source text, target text.
1. Introduction
One of the key issues in modern linguistics is natural language processing. Working with large
amounts of factual information enables the researcher to avoid subjective selection of facts for
confirming or rejecting the hypothesis. Nowadays there is a number of information technologies
enabling an automated search with the aim of forming the factual basis of the research, corpora of
texts being one of them. The corpus of texts is a central concept in corpus linguistics and its object of
study. The issues of corpus linguistics are widely ranged and involve studies of the general theory of
corpus linguistics, correlations of corpus linguistics and other linguistic disciplines, corpus typologies
and methods of corpus data interpretation, the principles of creating natural languages text corpora
(D. Biber [3; 3], J. Sinclair [28], W. Teubert [30], G. Kennedy [16], G. Leech [14; 20],
A. Stefanowitsch [29], T. McEnery [10; 14], D. Barth, S. Stefan [2], N.S. Dash, S. Arulmozi [11],
G. Desagulier [12], M. Paquot, S. Th. Gries [25], V. Shyrokov [9], O. Demska-Kulchytska [13],
A. Baranov [1] etc). Since a language is not a strictly arranged system and has probabilistic and
stochastic character, it is advisable to apply statistical methods in order to research it [17]. Research in
corpus linguistics is facilitated by special software tools – concordancers and corpus managers –
which provide various opportunities to obtain the necessary information from the corpus. Thus,
corpora allow addressing the variety of research questions and have been applied in a wide range of
linguistic disciplines, including lexicography, grammar, discourse analysis, sociolinguistics, language
COLINS-2022: 6th International Conference on Computational Linguistics and Intelligent Systems, May 12–13, 2022, Gliwice, Poland.
EMAIL: mariia.p.bekhta@lpnu.ua (M. Bekhta-Hamanchuk); halyna.d.oleksiv@lpnu.ua (H. Oleksiv); tetiana.v.shestakevych@lpnu.ua
(T. Shestakevych); yuliia.i.shyika@lpnu.ua (Ju. Shyika). ORCID: 0000-0002-3133-0948 (M. Bekhta-Hamachuk); 0000-0002-8800-6217
(H. Oleksiv); 0000-0002-4898-6927 (T. Shestakevych); 0000-0003-2474-0479 (Ju. Shyika).
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
teaching, literary studies, translation studies, pragmatics, cognitive linguistics, conceptual studies, etc
[26, p.473].
2. Theoretical background
Biber et al. define a corpus as “a large and principled collection of natural texts” [3, p.4].
Generally understood as the collection of texts, the term corpus can have different meanings in
various disciplines. In fiction studies, it is a collection of particular author’s works. In the field of
linguistics, the corpus refers to any collection of data (whether narrative texts or separate sentences)
obtained for the purpose of linguistic research, often taking into account a specific research goal [27,
p.769; 29, p.22]. But the term is used in a different way in corpus linguistics – “it refers to a collection
of samples of language use with the following properties:
the instances of language use contained in it are authentic;
the collection is representative of the language or language variety under investigation;
the collection is large” [27, p.769; 29, p.22].
Additionally, texts in such collections are often commented on in order to enhance their potential
for linguistic analysis. In particular, they may contain information about the paralinguistic aspects of
the source data (intonation, font style, etc.), linguistic properties of utterances (parts of speech,
syntactic structure) and demographic information about speakers / writers [29; p.22]. The volume and
content of the corpus may change, but these changes must neither influence its representativeness nor
change it reasonably. Search in the data corpus allows a researcher to build a concordance for any
word, i.e. to build a list of all usages of the word in the context and with the references to the source.
Corpora can be used to obtain a variety of data and statistics on language and language units.
As a rule, the research process within a corpus involves the following stages:
1. Selection of sources of linguistic material.
2. Data entry. Texts in electronic form with the extension .txt were included in the corpus.
3. Philological verification and texts editings.
4. Converting and graphematic analysis which includes recoding of nontextual elements or their
removal and division of the text into structural parts.
5. Providing texts and their components with additional information, i.e. text markup.
6. Converting of marked texts into the corpus and providing access to it.
To serve as a basis of the scientific research, a corpus should not only have a significant volume or
contain data of various types but also it should possess the following features:
Representativeness. The corpus must represent all the features of a particular area. It can be
very large (national corpus) or very small (author corpus). T. McEnery argues that the
representativeness of the corpora is caused by two factors: the set of genres that are in the
corpus and the selection of texts [10]. Selection is characterized by the limit of real material,
selecting certain parts of speech from the language array. However, the largest language
corpus can display only a small part of oral and written texts. Representativeness is closely
related to volume of the corpus. However, volume of the corpus is determined by two factors:
representativeness (sufficiency of texts (words) for accurate representation of the language
material) and practicality (accessibility and labour-intensiveness). For example, it is necessary
to cover all works of a certain author, or historical texts of a certain period, or texts of a
certain subject (for example, radio or TV series, political speeches). In other cases, full
representation of language cannot be achieved.
Balance. Corpus representativeness largely depends on how balanced a corpus is. The
acceptable balance of a corpus is determined by its intended uses. A balanced corpus usually
covers a wide range of text categories which are supposed to be representative of the language
or language variety under consideration. [10] Although balance is indispensable in corpus
design, there is no scientific method of measuring it. Nonetheless, text typology is of high
relevance if one attempts at corpus balance. To achieve balance, a corpus requires certain
characteristics of text selection, which include differences between the book and newspaper,
different genres of literature and authors.
Machine readability is the main criterion for electronic text corpus. Machine readability also
requires encoding of corpus data. Corpus computerization has many advantages. It speeds up
processing and makes working with data sets much easier. After computer processing of data,
the objective and accurate results are obtained. Machine readability enables further automatic
processing of data of a particular corpus, and allows the researcher to improve the corpus with
all sorts of markup. It is the use of computerized corpora, together with computer programs
which facilitate linguistic analysis, that distinguishes modern machine-readable corpora from
early corpora [10].
The purpose of the language corpus is to show the functioning of linguistic units in their natural
contextual environment. The following prerequisites form the basis for further creation and usage of
corpora:
1. substantial (representative) and balanced volume of the corpus guarantees the typicality of the
data and provides the whole spectrum of linguistic phenomena;
2. various data, which are included in the corpus, are in their natural contextual form, which
creates the possibility of their comprehensive and objective study;
3. once created and prepared data set can be used repeatedly, by different researchers and for
different purposes.
In the process of creating a corpus, the certain procedures should be followed, regardless of
whether the corpus includes spoken or written language material. Some of the issues that are optimal
in building the corpus include: typology of texts, file names and their format, etc. The next important
step in building a corpus is marking and annotation. Document markup refers to labeling, similar to
HTML code used to indicate features of a document: paragraphs, fonts, sentences, including sentence
numbers, author identification, and end-of-text markings. At the basic level, the title can be
considered as a type of markup as it provides additional information about the text.
Apart from corpus, another key term in corpus linguistics is corpus annotation, which is defined
by G. Leech as the process of “adding interpretative, linguistic information to an electronic corpus of
spoken and / or written language data [20]. The main issue in corpus linguistics is the creating of
means of automatic / automated text annotation based on different criteria – morphological, orthoepic,
semantic, syntactic, etc. V Shyrokov states that automated division of an electronic literary text into
‘microcontexts’ is the main idea of linguistic corpus engineering, with microcontexts being text
fragments grouped around the object under interpretation [9; p.99].
Corpus annotation can take many forms that can be implemented at different levels:
1. at the phonological level: the corpora can be commented on the constituent boundaries
(phonetic / phonemic annotation) or prosodic features (prosodic annotation);
2. at the morphological level: the corpora can be annotated as prefixes, stems and suffixes
(morphological annotation);
3. at the lexical level: the corpora can be annotated by parts of speech, lemmas (lemmatization)
and semantic fields (semantic annotation);
4. at the syntactic level: the corpora can be annotated to reflect anaphoric connections, pragmatic
information such as language acts (pragmatic annotation), or stylistic features such as speech
and thought representation (stylistic annotation).
The most common form of corpus annotation includes tags of the parts of speech (POS tagging or
grammatical tagging), which mark each word in the corpus as a grammatical category (e.g. noun,
adjective, adverb etc.). When corpora began to be annotated, the levels of annotation applied were
simple. However, as the tools evolved, more levels of linguistic knowledge started to be incorporated
into the texts and corpora [15, p.47]. These tags facilitate settling a number of issues about a simple
search for a particular keyword. Many words are ambiguous, but when a word is marked with a part
of speech, it eliminates ambiguity and helps focus the search results clearly. Therefore, annotated
corpora can be widely applied. Many linguistic analyses depend heavily on POS tagging [10].
To sum up, annotation aims at the addition of extralinguistic, structural, and linguistic special
markers to texts. Different types of linguistic markup are distinguished: morphological, syntactic,
semantic, anaphoric and prosodic. Also, the following procedures are carried out: tokenization,
lemmatization, stemming and parsing. Most corpora belong to the morphological or syntactic type. It
should be noted that the latter explicitly or implicitly contain morphological characteristics of lexical
units.
Since corpus linguistics uses large and representative samples of natural language texts for the
research, there are several types of software that can be used in the study. They are: concordancers
(LEXA, MonoConc, MicroConcord, TACT, WordSmith, WordCruncher, Manatee (Bonito), IMS
Corpus Workbench (CQP), XAIRA, LEXA, Virtual Corpus Manager (VMC), EXMARaLDA
Corpus–Manager (Co–Ma)) and a specific software for comprehensive analysis.
Concordancers are used to make lists of examples (occurrences) of the required token (tokens,
lemmas, morphemes etc) in the minimum context. Usually such a context is a fragment of several
linguistic units on the left (L) and on the right (R).
The corpus manager refers to the system for managing textual and linguistic data. It is a special
search system that uses software to search for data in the corpus, obtain statistical information and
provide results to the user in a convenient form. The results of this procedure are presented in the
form of horizontal lines with a search word in the middle. This process is called KWIC (Key Word In
Context) [18].
Corpus analysis software tools vary in functionalities, but all of them facilitate to search the
corpus for a specific set of linguistic units. Most of these software packages have the following
features:
1. they create KWIC (keyword in context) concordants, i.e. they display the query in their
immediate context, defined in terms of a certain number of words or symbols on the left and
right;
2. they identify the collocations of this expression, i.e. the forms of words that occur in a
particular position in relation to another word; these words are usually listed in the order in
which they occur in the appropriate position;
3. they form lists of frequencies, i.e. lists of all lines of symbols in the corpus, listed in the order
of their frequency.
Generally, modern software tools used in corpus linguistics research are fast and rich in features.
On the other hand, most of the tools are English-centric in that they only allow access to English
corpora. In addition, they all offer a different user-experience, because each tool is created in isolation
and thus offers a different user interface, control flow, and functionality [19, p.154]. Nevertheless,
corpus software tools are indispensable in corpus-based research projects.
3. Results and discussion
Text corpus, being the main issue of corpus linguistics, is widely applicable in translation studies.
This study focuses on the contrastive analysis of the quantitative parameters of the source (English)
text and its translation (Ukrainian). Jack London’s short stories collection “Children of the Frost” is in
the centre of attention. The choice has been made due to the fact that the literary work in question has
not been studied from statistical perspective before. In the process of analysis quantitative and
qualitative analytical methods have been used.
In this research the analysis of Jack London’s collection of short stories “Children of the Frost”
has been conducted on the basis of the digitally processed and marked up corpus of original texts and
Ukrainian translations by V. Hladka and K. Koriakina [22; 23]. It covers a number of characteristics
which are compared in Tables 1-4. Here and after, we propose some denotations, the text volume is
N, the number of different word forms is V, the number of sentences is S, the number of letters is C,
the number of content words is C1, the number of functional words is F1, the number of Hapax
legomena is V1, the number of words in the text with a frequency of 10 or more is N10.
Table 1
Quantitative parameters of source and target texts
Coefficient Source text Target text Ratio
N 45678 32192 1,42
V 11790 16263 0,72
S 3185 3527 0,90
C 210423 199852 1,05
C1 31418 28755 1,09
F1 14260 8699 1,64
V1 6453 7853 0,82
N10 725 575 1,26
The visualization (Fig. 1) of the data from the Table 1 is performed to show the ratio between the
quantitative characteristics of the Source and Target texts. Here each quantitative characteristics of the
Source text has been divided by the appropriate number that characterizes the Target text. When the
result of such division is above 1, it means the appropriate characteristic of the Source text exceeds
the Target text.
2,00
1,50
1,00
0,50
0,00
F1 N N10 C1 C S V1 V1
Figure 1. The ratio of Source text and Target text characteristics
As is seen from the Figure 1, in the process of translation, the number of functional words
decreased, as well as text volume and Number of words in the text with a frequency of 10 or more.
The number of different word forms is higher in the Target text, which is predictable at least because
the Ukrainian language has seven cases, as opposed to two cases in English.
Table 2
Quantitative parameters of original stories
Coefficient In the The Nam- The The The Keesh, The Li The
Fore- Law bok Master Sun- Sick- Son of Death Wan, League
sts of of the of lan- ness of Keesh of the of the
the Life Unve- Mystery ders Lon Ligoun Fair Old
North racious Chief Men
N 5970 2836 4500 4085 6368 3632 3135 3610 5249 6293
V 1485 916 1059 1275 1369 906 898 903 1472 1507
S 477 193 379 295 463 180 254 186 413 345
C 28372 11673 22882 17933 32653 14505 15675 14421 26397 25912
C1 4200 1992 3049 2968 4202 2533 2121 2423 3548 4382
F1 1770 844 1451 1117 2166 1099 1014 1187 1701 1911
V1 890 444 654 678 806 372 557 372 981 699
N10 95 41 78 70 102 66 51 54 74 94
Table 3
Quantitative parameters of translated stories
Coefficient In the The Nam- The The The Sick- Keesh, The Li The
Fore- Law bok the Master Sun- ness of Son of Death Wan, League
sts of of Unve- of lan- Lon Chief Keesh of the of the
the Life racious Mystery ders Ligoun Fair Old
North Men
N 5512 2155 3271 3487 4627 2950 2221 2713 5264 5256
V 2487 1175 1212 1655 1632 1323 995 1295 2297 2192
S 529 214 423 324 526 187 277 202 461 384
C 27138 10499 21960 17132 31400 13580 14694 13189 25427 24833
C1 4382 1705 2311 2736 3287 2310 1642 2105 4159 4118
F1 1130 450 960 751 1340 640 579 608 1103 1138
V1 1022 524 861 717 1107 516 715 529 973 889
N10 85 33 50 58 61 55 29 46 75 83
Table 4
Ratio of characteristics of the source text and target text
Coefficient In the The Nambok the The The The Keesh, The Li The
Forests Law Unveracious Master Sunlan- Sick- Son of Death Wan, League
of the of of ders ness Keesh of the of the
North Life Mystery of Ligoun Fair Old
Lon Men
Chief
N 1,1 1,3 1,4 1,2 1,4 1,2 1,4 1,3 1,0 1,2
V 0,6 0,8 0,9 0,8 0,8 0,7 0,9 0,7 0,6 0,7
S
0,9 0,9 0,9 0,9 0,9 1,0 0,9 0,9 0,9 0,9
C 1,0 1,2 1,3 1,1 1,3 1,1 1,3 1,2 0,9 1,1
C1 1,6 1,9 1,5 1,5 1,6 1,7 1,8 2,0 1,5 1,7
F1 0,9 0,8 0,8 0,9 0,7 0,7 0,8 0,7 1,0 0,8
V1 1,1 1,2 1,6 1,2 1,7 1,2 1,8 1,2 1,0 1,1
The analysis of general characteristics has shown that the number of word usages in the source
text exceeds the number of word usages in the target text both in the whole corpus and in separate
stories. Altogether, the volume of the source text is 20.69% larger than the volume of the target text. It
should be noted that this contradicts the theory of translation S-universals and T-universals, which
was put forward by A. Chesterman [7], and involves an increase in the volume of the target text
compared to the source text.
The visualization (Fig. 2) of the data from the Table 2 and 3 is performed to show the ratio
between the quantitative characteristics of each Source and Target texts. Here each quantitative
characteristics of the Source text has been divided by the appropriate number that characterizes the
Target text. When the result of such division is above 1, it means the appropriate characteristic of the
Source text exceeds the Target text.
Figure 2. The ratio of each Source text and Target text characteristics
S-universals are observed when comparing the source text with a number of translations in some
target language. T-universals appeared as a result of comparing the corpora of target texts and course
texts. The following S-universals can be distinguished: increasing the volume of the translated text
compared to the original; simplification at the syntactic level; simplification at the lexical level –
reduction of lexical diversity and the tendency to use more frequent words in the target language;
reduction or avoidance of recurrences in the target language; avoiding the ethnospecific units in
translation; standardization (use of typical target language structures); convergence (translated texts
show greater linguistic similarities with each other than with the original texts).
As for T-universals, their taxonomy includes:
simplification (reduction of lexical diversity and density);
conventionalization (standardization);
atypical (unstable) lexical patterns [7].
The frequency of each part of speech in the text and the vocabulary of the author (translators) has
been compared since the ratio of parts of speech is an important statistical parameter of the individual
style of both the author and a particular work (Table 5).
The most frequent in the source and target texts are the functional words (5% of the vocabulary in
the source text and 6.37% in the target text). These words function most actively and cover almost a
quarter (29.81% in the original text and 23.31% in the translated text) of the text. Pronouns have
similar high activity in the text (3.18% of the vocabulary in the source text and 3.24% in the target
text). Pronouns cover about 13% of the text. Approximately the same share in the text and the
vocabulary is covered by adverbs (7.20% and 8.91% in the source text and 10.13% and 12.16% in the
target text) and numerals (0.91% and 1.07 in source text and 1.26% and 1.06% in the target text) (see
Table 6). In Figure 3, each quantitative characteristics of the Source text was divided by the
appropriate number that characterizes the Target text, as it is calculated in Table 7. When the result of
such division is above 1, it means the appropriate characteristic of the Source text exceeds the Target
text.
Table 5
Part of speech frequency in the source text
Part of In the The Nam- The The The Keesh, The Li Wan, The
speech Fore- Law bok Master Sunlan- Sick- Son of Death the Fair League
sts of of the of ders ness Keesh of of the
the Life Unve- Mystery of Ligoun Old
North racious Lon Men
Chief
Noun 1533 570 1070 897 1494 760 767 754 1243 1369
Adjective 556 243 293 325 404 336 258 274 423 557
Pronoun 602 331 524 508 597 476 334 489 574 793
Adverb 565 221 408 352 646 257 255 181 424 457
Verb 887 592 731 844 985 676 478 664 863 1148
Numeral 57 35 23 42 76 28 29 61 21 58
Preposition 819 317 618 471 877 420 454 474 748 734
Conjunction 429 180 325 282 511 326 202 378 414 575
Particle - - - - - - - - - -
Interjection 19 11 16 17 9 32 24 16 10 14
Article 503 336 492 347 769 321 334 319 529 588
Table 6
Part of speech frequency in the target text
Part of In the The Nam- The The The Keesh, The Li Wan, The
speech Fore- Law bok Master Sunlan- Sick- Son of Death the Fair League
sts of of the of ders ness Keesh of of the
the Life Unve- Mystery of Ligoun Old
North racious Lon Men
Chief
Noun 1341 529 697 808 1087 673 551 717 122 1251
Adjective 411 192 155 229 248 232 154 175 373 440
Pronoun 977 316 389 605 431 532 268 426 967 875
Adverb 542 225 370 353 514 283 214 205 521 505
Verb 1051 408 674 707 921 558 433 533 1044 980
Numeral 60 36 26 34 86 32 22 49 33 67
Preposition 476 176 339 323 457 267 227 292 468 510
Conjunction 429 177 424 259 693 261 251 237 436 473
Particle 210 94 189 153 177 104 95 78 193 149
Interjection 15 3 8 16 13 8 6 1 6 6
Article - - - - - - - - - -
Table 7
Ratio of part of speech frequency of source and target texts
In the Nam- The The
The The Li
Fore- The bok The Sick- Keesh, League
Part of Master Death Wan,
sts of Law of the Sunlan- ness Son of of the
speech of of the
the Life Unve- ders of Lon Keesh Old
Mystery Ligoun Fair
North racious Chief Men
Noun 1,1 1,1 1,5 1,1 1,4 1,1 1,4 1,1 10,2 1,1
Adjective 1,4 1,3 1,9 1,4 1,6 1,4 1,7 1,6 1,1 1,3
Pronoun 0,6 1,0 1,3 0,8 1,4 0,9 1,2 1,1 0,6 0,9
Adverb 1,0 1,0 1,1 1,0 1,3 0,9 1,2 0,9 0,8 0,9
Verb 0,8 1,5 1,1 1,2 1,1 1,2 1,1 1,2 0,8 1,2
Numeral 1,0 1,0 0,9 1,2 0,9 0,9 1,3 1,2 0,6 0,9
Preposition 1,7 1,8 1,8 1,5 1,9 1,6 2,0 1,6 1,6 1,4
Conjunction 1,0 1,0 0,8 1,1 0,7 1,2 0,8 1,6 0,9 1,2
Interjection 1,3 3,7 2,0 1,1 0,7 4,0 4,0 16,0 1,7 2,3
Nouns, verbs and adjectives are the most frequent; their relative number in the vocabulary, on the
contrary, exceeds the relative number in the text both source and target. These parts of speech
represent the vocabulary richness of the source and target texts and their ratio confirms that the
nominal character of the individual style of the original text has been preserved in the translation.
1,0
0,0
In the Fore-sts of the North The Law of Life Nam-bok the Unve-racious
The Master of Mystery The Sunlan-ders The Sick-ness of Lon Chief
Keesh, Son of Keesh The Death of Ligoun Li Wan, the Fair
The League of the Old Men
Figure 3. The ratio of each Source text and Target text characteristics (part of speech)
Linguistic and statistical analysis of the corpus under research has been carried out according to
the formula developed by S. Buk [5]. The following characteristics of the corpus have been
calculated:
The average word length in source and target texts which is calculated as the total number of
letters divided to the total number of words;
The average frequency of the word in the text (A), which is calculated as the volume of the
text (N) divided to the volume of the dictionary of tokens (V). This value is inverse to the index of
diversity and is calculated according to the formula (1). In our case, each word of the source texts
is repeated at least thrice, and in the target texts – at least twice.
𝐴 = 𝑁/𝑉 (1)
Exclusivity index of the text (Eт) is calculated as a number of words with a frequency of 1
(such words are referred to as hapax legomena) (V1) to the total volume of text (N). The formula
is the following:
Eт = V1 / N (2)
Exclusivity index of the vocabulary (Ec), i.e. the total number of separate words reduced to
the original form (V) is calculated according to the formula:
Ec = V1 / V (3)
The richness of the vocabulary (B) or in other words the index of diversity is calculated as the
volume of dictionary of tokens (V) to the volume of text (N). the formula is the following:
B = V/N (4)
The higher the index of diversity is, the bigger amount of diverse words the author or the translator
used in a particular text. In our case, the index equals 0,264 in the source text and 0,443 in the target
text. These indices are high enough, since according to S. Buk, the average index for fiction equals
0.067. [6]
Concentration index is a value opposite to the index of exclusivity and indicates what share of
the text (N) or vocabulary (V) is taken by highly frequency vocabulary (with absolute frequency of
10 or more). Concentration index is calculated according to the formulas: V10т / N is the text
concentration index and V10 / V is the vocabulary concentration index.
Index of lexical density (L) is calculated as the ratio of the number of different words to the
total number of words in the text. The algorithm for calculating the index of lexical density
includes the following steps: defining an input set of words (either a meaningful text or a part of it,
or a random set of words); conversion of each word into its vocabulary form (stemming); deleting
all duplicates. The formula for calculating lexical index is
L=K/N (5)
where N stands for the number of words after stemming and K stands for a number of words after
deleting the duplicates.
The automated readability index (ARI) is a measure of the complexity of a reader’s
perception of a text. ARI index is calculated according to the formula:
𝐶 𝑊 (6)
ARI = 4.71 × + 0.5 × − 21.43,
𝑊 𝐶
where C is the number of letters and numbers in the text, W is the number of words in the text and
S is the number of sentences in the text. The degree of aggression is the same in the source and
target texts and equals 0.19. This confirms the fact that the nominal character of the original text is
accurately reproduced in the translation.
The index of epithetization (Inat), as follows from its definition, indicates the ratio between
the total number of nouns in the text (Vn) ant the total number of adjectives (Vadj). The index of
epithetization is calculated according to the formula:
Inat = Vn / Vadj (7)
The higher the index of epithetization is, the fewer adjectives per noun are present. It can be
concluded that this index in source and target texts does not differ significantly: 2.86 / 3.51, and
therefore the translator was able to maintain the saturation of the text with figurative phrases.
The index of verb phrases shows the ratio between adverbs and verbs in the text. The original
texts have a slightly bigger percentage: 0.47 adverbs per 1 verb, while in translation – 0.51 per 1.
Nominality degree shows the ratio between nouns and verbs in the text. In the original texts,
there are 1.32 nouns per verb, in translation – 1.22 per 1.
The average sentence size indicates the peculiarities of verbal intelligence or a radical change
of emotional state. There is a negative correlation between the increase of emotionality of speech
and the amount of speech. In other words, the more emotional the speaker is, the shorter their
statements are.
The coefficient of aggression represents the ratio between the number of verbs (and
participles) and the total number of the words in the text. The coefficient is calculated according
the formula:
Aggression coefficient = N verbs / N of all words × 100%, (8)
where N – number of appropriate words.
High coefficient of aggression indicates considerable emotional tension of the text, dynamics of
events, poor emotional state of the author during text synthesis.
The coefficient of logical coherence represents the ratio between the total number of function
words (prepositions and conjunctions) and the total number of sentences in the text. Values within
1 show a fairly harmonious ratio between function words and syntactic constructions in the text.
The coefficient of logical coherence = N service words / N sentences, (9)
where N – number of appropriate words.
The coefficient of embolism means pragmatic tagging or clogging of speech and represents
the ratio between the total number of emboli (words that do not have semantic meaning) and the
total number of words in the text. Such words include interjections, vulgarisms, repetitions, etc.
The coefficient of embolism negatively correlates with the indicators of verbal intelligence and the
degree of emotional excitement of the speaker / author of the text. The coefficient of embolism is
calculated according to the formula:
Embolism ratio = Nembol / All words × 100%, (10)
where N – number of appropriate words.
The quantitative indices, which have been calculated on the basis of the general characteristics of
the source and target texts, have been compared (Table 8).
Table 8
Quantitative indices in the source and target texts
Coefficient The average value in The average value in Ratio of average
the corpus of the the corpus of the values of source
source text target text text and target text
Average word length 4,56 5,381 0,8
Average word frequency 3,846 2,294 1,7
Vocabulary exclusivity 0,538 0,503 1,1
index
Diversity index 0,264 0,443 0,6
Exclusivity index for text 0,144 0,217 0,7
Vocabulary 0,058 0,0731 0,8
concentration index
Lexical density index 0,69 0,765 0,9
Automatic readability 7,229 9,87 0,7
index
Index of epithetization 2,86 3,511 0,8
Index of verb phrases 0,472 0,51 0,9
Degree of nominality 1,322 1,22 1,1
An average sentence 14,956 11,012 1,4
size
Coefficient of aggression 0,194 0,196 1,0
Coefficient of logical 3,113 2,083 1,5
coherence
Coefficient of embolism 0,0037 0,0415 0,1
As presented in table 8, the main indicators that characterize the individual style in the source and
target texts, do not differ significantly (Figure 4), except of average word frequency, which in source
text is almost twice higher, and the coefficient of embolism is ten times higher in a target text, then it
is in source text.
1,8 1,7
1,6 1,5
1,4
1,4
1,2 1,1 1,1
1,0
1,0 0,9 0,9
0,8 0,8 0,8
0,8 0,7
0,7
0,6
0,6
0,4
0,2 0,1
0,0
Figure 4. Indicators characterizing the individual style in source and target texts
To determine the significance / insignificance of the statistical difference between the values of the
indices, t-criterion has been calculated, using the appropriate functions in Excel. For the given data on
our samples, the t- criterion equals 0,69.
To decide whether the t-criterion indicates a significant difference, it is necessary to evaluate it
according to the table of critical values of t. This evaluation is carried out by determining the number
of degrees of freedom, which in our case f = 15-2 = 13 (the number of indicators subtract the number
of samples under comparison). The difference is considered significant if the calculated value of t is
greater than the tabular value for a given level of significance. In our case, 0,69 is less than the
smallest number in rows. This means that the difference in the statistical indicators of the source and
target texts is insignificant and statistically acceptable.
4. Conclusion
All in all the paper presents the quantitative comparative study of the collection “Children of the
Frost” by J. London and its Ukrainian translation by V. Hladka and K. Koriakina, which have not
been analyzed from the statistical viewpoint before. Concluding the research it can be noted that:
the number of word usages in the source text exceeds the number of word usages in the target
text both in the whole corpus and in separate stories. In general, the volume of the source text is
bigger than the volume of the target text by 20.69%;
indices of vocabulary richness, exclusivity for the text and the vocabulary, the concentration
of the vocabulary do not differ significantly;
the mainly used parts of speech in English and Ukrainian texts are nouns (22.22% and
25.21%), verbs (19.27% and 18.89%), adjectives (7.7% and 7.64%) and adverbs (7.2% and
7.64%);
the translation preserves the ratio of different parts of speech. The number of pronouns,
adverbs and functional words in the vocabulary of the target text has slightly decreased;
the index of epithetization which indicates the number of nouns per adjective in the text, does
not differ significantly in source and target texts – 2.86 / 3.51;
the index of verb phrases shows the number of adverbs per verb in the text. The index is
higher in the source text 0.47 adverbs per 1 verb, while in target text 0.51 per 1;
degree of nominality shows the number of nouns per verb. In the source text, there are 1.32
nouns per verb, in the target text – 1.22 per 1. Therefore, the degree of aggression, which is
calculated as the ratio of the number of verbs and verb forms (particles) to the total number of the
words, is identical in source and target text and equals 0.19. This confirms the fact that the
nominal character of the source text has been accurately reproduced in the target text.
Various linguistic disciplines will benefit from the research findings. These findings can be
applicable in the analysis conducted within the scope of corpus linguistics, translation studies, literary
studies, discourse analysis, lexicography etc.
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