=Paper=
{{Paper
|id=Vol-2552/Paper11
|storemode=property
|title=Quantitative Analyses of Using Adjectives of Color in Russian Poetic Texts
|pdfUrl=https://ceur-ws.org/Vol-2552/Paper11.pdf
|volume=Vol-2552
|authors=Andrey Masevich,Victor Zakharov
}}
==Quantitative Analyses of Using Adjectives of Color in Russian Poetic Texts ==
https://ceur-ws.org/Vol-2552/Paper11.pdf
Quantitative Analyses of
Using Adjectives of Color in
Russian Poetic Texts∗
Andrey Masevich1 Viktor Zakharov2
andmasev@mail.ru v.zakharov@spbu.ru
1
Saint-Petersburg State Institute of Culture,
2
Saint Petersburg State University,
Saint Petersburg, Russia
Abstract
The paper deals with frequency behavior of adjectives of color, which was researched
on the base of semantic annotation of Russian National Corpus (RNC) and standard
computer technologies (MS Excel). The authors suggest a classification scheme for color
adjectives tagged with semantic markup of RNC. The obtained results show the model of
frequency behavior of color adjectives in poetic texts within a period from 1750 to 1999.
It was revealed that IPM of color adjectives grows significantly at the beginning of 20th
century. The frequency behavior of these adjectives correlates to their classes.
Keywords: Russian National Corpus, semantic annotation, adjectives of color, clas-
sification, frequency behavior, poetic texts, diachronic studies
1 Introduction
The quantitative characteristics of linguistics models and the notable frequency behavior
of words give an idea of the real functioning of these models and add as much as possible to
completeness, clarity and certainty.
The key research tool is the semantic annotation of the Russian National Corpus (RNC).
However, it should be borne in mind that the semantic annotation of the RNC, implemented
automatically, is still imperfect. Below we will show what its imperfection lies in. This raises
the question of how the imperfection of annotation affects the results and, accordingly, how it
should be taken into account in their evaluation and interpretation. We do not have an exact
answer, although there is some indirect evidence in favor of the reliability of our results.
∗
Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License Attri-
bution 4.0 International (CC BY 4.0).
1
Figure 1: Comparison of IPM of adjectives, abstract nouns and adverbs denoting color in the
four corpora RNC
The paper considers the frequency behavior, i.e. the change in the frequency of the use
of adjectives of color (coloratives) in Russian poetic texts over a significant period of time –
from 1750 to 1999. The work continues our previous studies [Masevich, Zakharov, 2019].
In addition to presenting the results of the study, the article demonstrates some possibili-
ties of corpus linguistics and offers a methodology of semantic research using linguistic corpora
and standard computer technologies.
2 Material Under Study
In this paper we consider quantitative characteristics of adjectives of color (i.e. adjectives
denoting color) in Russian poetic texts. Our research is based on the material of the RNC
corpora, mainly on the poetry corpus (PC RNC).
The choice for the study of poetic texts and adjectives is due to the fact that it is in
them that the designation of color, direct or metaphorical, plays a more significant role than
in any other literary or speech works, which is reflected in the frequency behavior of words of
different lexical groups.
Colors are described by different content words – nouns, adverbs, and verbs, but the
main function of describing color is performed by adjectives. Let us present in the form of a
histogram the results of the research into the four corpora of the RNC (Fig. 1).
From Fig. 1 it is clear that the relative frequency (IPM) of color words related to adjec-
tives, nouns and adverbs in the poetic corpus is much higher than in the other three corpora.
It is also clearly seen that the IPM of adjectives is higher than in other parts of speech.
Unfortunately, in the RNC search menu for the verbs such characteristics as “color” is
missing. Therefore, we selected 13 of the most characteristic verbs that contain the meaning
of color, and compared their frequencies in the same four cases of RNC (Table 1).
From these data, it can be seen that the frequency of words meaning colors (or words
2
with the tag “color”) in the poetic corpus is also higher than their frequency in other corpora.
Thus, we can definitely state that the frequency of the terms of color in poetic texts is
higher than in any genre of prose.
3 Semantic Annotation of RNC
The RNC demonstrates the first (or at least one of the first) experience of semantic an-
notation of the national corpus in the short history of corpus linguistics [Kustova et al., 2005].
Detailed description of the marking is available on the RNC website (see http://www.
Table 1: Frequency behavior of 13 verbs denoting color in the four corpora of the RNC
Frequency
Verb Main corpus Newspaper corpus Oral corpus Poetic corpus
Number NPM Number NPM Number NPM Number NPM
of occur- of occur- of occur- of occur-
rences rences rences rences
белеть 3356 11,84 159 0,70 20 1,65 897 81,79
(to whiten)
чернеть 2968 10,47 134 0,59 6 0,50 733 66,84
(to blacken)
краснеть 5090 17,96 704 3,08 65 5,37 443 40,39
(to redden)
зеленеть 1948 6,87 184 0,81 12 0,99 501 45,68
(to turn green)
желтеть 1353 4,77 135 0,59 12 0,99 315 28,72
(to turn yel-
low)
синеть 1923 6,78 178 0,78 3 0,25 691 63,01
(to turn blue)
сереть 568 2,00 61 0,27 1 0,08 123 11,22
(to turn gray)
розоветь 756 2,67 29 0,13 2 0,17 219 19,97
(to turn pink)
коричневеть 20 0,07 2 0,01 0 0,00 3 0,27
(to turn brown)
оранжеветь 3356 0,01 0 0,00 0 0,00 1 0,09
(to turn orange)
буреть 2968 0,52 41 0,18 3 0,25 7 0,64
(to turn brown)
алеть 5090 2,64 53 0,23 11 0,91 337 30,73
(to turn scarlet)
лиловеть 1948 0,35 1 0,00 0 0,00 45 4,10
(to turn purple)
Average IPM 5,15 0,57 0,86 30,27
3
ruscorpora.ru/new/corpora-sem.html).
The purpose of the RNC semantic annotation is to create the ability to search for groups of
lexical units that have a common semantic attribute. The RNC semantic annotation is based
on the system of classification Russian lexicon, which is adopted in the Lexicograph database
[Krasil’shchik, Rakhilina, 1992]. The classification is based on the facet-hierarchical principle,
according to which the classification index can combine several features or classes (facets), and
each class includes several subclasses of features. To each lexical unit of the corpus a certain
class of attributes is assigned in form of an annotation code. Thus the adjective красный (red)
can be attributed to the class “physical properties”, which includes the sign “color”, the code
(tag) t:physq:color [Kustova et al., 2005]. Semantic signs are attributed to all word forms
of significant words of the corpus.
By an attributed sign or by a class of signs (classification index), a query can be specified
and a search can be made to retrieve contexts of words corresponding to a given query.
Each word in the corpus has a full description containing grammatical, semantic and
some other features, presented in the form of codes (tags). Descriptions have areas: lemma,
grammar, basic semantics, additional semantics and additional attributes. The description is
called up by clicking the mouse when the cursor is positioned on the desired word (Fig. 2).
Figure 2: Description of word form красной (red) in the poetry corpus of RNC
When searching, semantic attributes are set in the search query window using the menu
tabs that are opened by the link “Semantic signs: choose”. Each menu corresponds to a specific
part of speech (see the menu for adjectives, Fig. 3).
The eight menus are provided with a list of semantic attributes for the five parts of speech.
Three menus correspond to nouns, for subject, non-subject, and proper names. The semantic
4
Figure 3: The menu for the selection of semantic attributes for adjectives
attribute “color” can be defined for three parts of speech – adjectives, non-subjective nouns
and adverbs.
4 Classification of Adjectives with the “Color” Attribute
The texts from the poetry corpus of the RNC for the period from 1750 to 1999. contain
99154 adjectives with the tag “color” (t:physq:color). Our verification, however, showed
that not every adjective that has or may have a meaning of “color” actually has this tag. It
depends largely on which class an adjective belongs to.
In our work [Masevich, Zakharov, 2019], we proposed a classification of color adjectives,
which we give below. The classification of colorants is essential for this work, since adjectives
of different classes show different quantitative distributions by chronological periods and by
authors. Classification is an integral part of the color picture of world.
4.1 Adjectives Meaning Color Per Se
These are adjectives that indicate the main tone (hue), i.e. “the attribute of colors
that permits them to be classed as red, yellow, green, blue, or an intermediate between any
contiguous pair of these colors” (Webster dictionary). We have revealed 28 of them in the
poetry corpus of RNC. Almost all adjectives of this type in the RNC have the tag “color”. The
only exception that we were able to identify was the adjective рдяный (vermillion, scarlet).
This class includes, first of all, primary colors, or color categories. Different authors
5
refer to different sets of color designations. We rank to the main adjectives (or basic color
categories) a set of twelve lexemes: белый (white), черный (black), красный (red), желтый
(yellow), зеленый (green), голубой (light blue), синий (blue), коричневый (brown), серый
(gray), розовый (pink), оранжевый (orange), and фиолетовый (purple). Beside adjectives
belonging to basic categories, this class includes other adjectives meaning colors per se, e.g.
алый (scarlet), лазурный (azure) etc.
Note. There is a difficulty in describing and classifying Russian terms of color in English
(and surely in other languages too), since the system of color nomination and semantics of
colors in Russian are very different from English. It causes challenges interpreters have to deal
with.
From adjectives of colors per se, as a rule, derivatives are easily formed: a compara-
tive degree, белее (whiter or more white), чернее (blacker), краснее (approximately, more
red); nouns, белизна (whiteness), желтизна (yellowness), синева (approximately, blueness);
verbs, белеть (whiten), чернеть (blacken), зеленеть (to turn green), adverbs черно (black),
сине (blue), бело (white).
The verbs and nouns of color in English are very few. For example the Russian verb
синеть according to RNC parallel corpus is translated most frequently as to turn blue or less
frequently to be blue.
However, not all adjectives indicating the main tone possess this property. For exam-
ple, adjectives such as лазурный (azure), бежевый (beige), палевый (fawn), фиолетовый
(purple), etc. do not have derivatives (and in some cases, they may have been lost).
When working with terms of color one should remember that the contexts differ when
they are used in a direct meaning or in a figurative or phraseological meaning. At this stage of
the study, with some exceptions, we do not consider this difference. Quantitative accounting
of different contexts is a methodically and technically difficult task that can be solved only by
viewing significant arrays of contexts.
In [Masevich, Zakharov, 2019] we showed a significant number of standard collocation
and phraseological units with adjectives of color, for example, белый билет, белая гвардия,
белая баня, шито белыми нитками, белое духовенство, белая ворона, белый свет, красное
лето, красная девица, красное словцо, красное дерево, красная строка, зеленая скука,
зеленая улица, синий чулок, желтый дом, желтая пресса, желтая лихорадка, серые
будни, серая личность, коричневая чума, оранжевая революция, чёрный день, чёрный
ход, чёрная работа, черный рынок, чёрная зависть, розовые надежды. We also note that
such expressions are formed exclusively with adjectives meaning the main color categories.
Note. Some of the expressions one cannot literally translate into English, e.g. the standard
collocation красная девица literally means red girl or girl of red color but the real meaning is
beautiful girl. The others have in English different meaning, e.g. красная нить (red string) in
Russian means the main topic of something and in English it has several different meanings,
one of them is “a thin red string worn to ward off misfortune”.
However, some expressions can be translated literally, e.g. желтая пресса (yellow press),
коричневая чума (brown plague), оранжевая революция (orange revolution), черный рынок
(black market).
6
4.2 “Quasi-Colors” Adjectives
For the convenience of presentation, we have designated a certain type of adjectives,
which have tag “color” by the term “quasi-colors”. These are words that characterize a
color, but do not mean it, in other words, do not indicate a hue, for example: темный
(dark), светлый (light), бледный (pale), пестрый (motley), сумрачный (somber), цветной
(colored), разноцветный (multi-colored), бесцветный (colorless), пятнистый (spotty),
клетчатый (checkered), блеклый (faded), трехцветный (tricolor), крапчатый (speckled)
etc. These adjectives describe various visual characteristics of the object – except, perhaps,
its shape.
R.M. Frumkina cites the work of Heller and Makris Parametric Linguistics [Heller, Makris, 1967]
where it is argued that the essential component of color is the basic tone (Eng. hue). “Any
CN (color name – auth.) сontains an indication of color in the proper sense (i.e. tone, hue),
but there are no CNs that would contain information about brightness and saturation without
indicating a color (tone). Indeed, there are no such CNs simply because we do not consider
CNs light, bright, dull, brilliant, and motley” [Frumkina, 1984, p. 20].
From a certain point of view, we can talk about a systemic error in the semantic annotation
of RNC – assigning the tag “color” to words that do not mean it. However, in the course of the
study, the presence of a tag that allows identifying and grouping adjectives with this meaning
turned out to be, in our opinion, useful and important for describing the color linguistic
picture of the world. In this case, one cannot speak about color names (CNs according to
R.M. Frumkina), but about color words, i.e. about words related to color.
Unfortunately, we have to admit to some extent the inconsistency of the semantic mark-
up of the RNC. We used synonyms dictionaries, as well as a system for generating semantic
fields of color-words based on texts, which allowed us to identify a significant number of words
of this type that do not have a semantic marking tag in RNC, for example, матовый (matte),
яркий (bright), радужный (rainbow), узорный (patterned), узорчатый (patterned), хмурый
(gloomy) etc.
4.3 Analog Adjectives of Color
Some authors call these adjectives the colors of secondary nomination [Dyupina et al., 2013]
(relative adjectives). In the studied array of texts from the poetry corpus, only 12 adjectives
that have this tag were identified, including: агатовый (agate), аметистовый (amethyst),
белоснежный (snow-white), бирюзовый (turquoise), дымчатый (smoky), златой (golden),
изумрудный (emerald), малахитовый (malachite) etc.
Unlike adjectives of colors per se and quasi-colors, analog adjectives, as a rule, do not have
a comparative degree and derivatives verbs, adverbs and nouns. However, they can form one
of the roots of a complex adjective алмазно-синий (diamond blue), молочно-белый (milky
white), янтарно-лиловый (amber purple) etc. They also enter, usually in a metaphoric sense,
into stable phrases: золотые слова (golden words), золотое время (golden time) etc.
At the same time, in addition to the above 12 adjectives, in the RNC poetry corpus we
identified about 20 adjectives (more than 1000 usages) of this type that do not have the color
tag, e.g. васильковый (cornflower), вишневый (cherry) etc.
The conclusion, it would seem, is clear: the tag “color” should be ascribed to all analog
adjectives. Analysis of the word малиновый (raspberry-red) (114 random contexts out of 258
7
found) showed that in 82 contexts this adjective denotes color.
The remaining uses bear the meanings of “food” (5 contexts), “sound” (9 contexts), “plant”
(5 contexts), and metaphorical meaning (13 contexts). However, not every adjective of this
class has a dominant color meaning. For such adjectives as кровавый (bloody-red), стальной
(steel-colored), this meaning would be rather peripheral, as a result of which the use of the
tag could cause significant information noise. Therefore, the issue, in our opinion, requires
further study.
4.4 Adjectives Meaning Specific Colors of Certain Objects (Object-
Related)
Object-related adjectives or adjectives with limited cooccurrence range describe ani-
mal coloration, the color of human eyes or hair (карий – brown, рыжий – red) or surface
(крапчатый – spotty). They are strictly linked to the object, such as a certain animal. e.g.
with horse – гнедой (bay), вороной (raven-black), буланый (cream-colored), саврасый (dun)
and are usually not used with other objects. But there are some counterexamples. Thus, the
adjective пегий (spotted) one can sometimes use in relation to dogs (like in Chingiz Aitma-
tov’s novel Spotted Dog Running along the Bank of the Sea), as well as to rabbits, deers, birds
and even to fishes. We cannot say that the adjective пегий (spotted) is used only with living
creatures. In poetry, we can see such images as “Дорога пега – быть теплу” (Н. А. Клю-
ев. “Зима изгрызла бок у стога...”, 1915) ‘the road is spotted, time to be warm’ (Nikolai
Klyuyev’s The Winter Bit the Haystack’s Side, 1915) or “Галстук вывязан и без спеха – в
меру ряб или в меру пег” (Б. А. Слуцкий. “Новый тип”, 1970–1975) ‘the tie is knitted with
no hurry, a bit spotted or a bit freckled’ (Boris Slutzky’s New Type Personality, 1970–1975).
But such word uses are rare and can be explained as metaphors.
The field of use for such adjectives as крапчатый (speckled) or рыжий (red, red-headed)
is much broader. The most frequent bigrams with the adjective рыжий are рыжий мужик
(red-headed man), рыжий конь (red horse), рыжий кот (red cat) and рыжий детина (red-
headed guy). These adjectives are also used for descriptions of stock objects (speckled surface,
speckled ornament, red trousers, red polish). Taking this into account with the fact that the
adjective рыжий (red, red-haired) shows the hue, we classified it as a color per se, not as an
object-related adjective, while the adjective крапчатый (speckled), which does not show the
hue, we classified as a quasi-color.
4.5 Derivatives of Color Adjectives
4.5.1 Diminutives
This class includes such adjectives as беленький (diminutive of white), желтенький
(diminutive of yellow) etc. and quasi-color derivatives such as пестренький (diminutive of
motley) etc. The adjectives in this form are practically untranslatable into English, for they
do not mean the color of thing only, but to a bigger degree the psychological attitude to it,
from tenderness and sympathy to condescension and even light scorn.
8
4.5.2 Adjectives of Likeness
Here, we mean adjectives like зеленоватый (similar to green, greenish), желтоватый
(similar to yellow, yellowish), красноватый (similar to red, reddish), темноватый (rather
dark), бледноватый (a bit pale). Like diminutives adjectives, these adjectives do not have
an exact English equivalent and mean likeness to a certain color (or resemble a certain color).
We include in this class also verbal adjectives, which show the result of some process. These
are words like посинелый (became blue), почернелый (turned black), потускнелый (became
lackluster).
4.5.3 Double (Compound) Color Adjectives
As we have already noticed in the previous study [Masevich, Zakharov, 2019], double (as
well as triple and more) adjectives are the most numerous group, but every word has low
frequency. It should be noticed that the RNC tags these adjectives according to two random
rules: in some cases, a compound adjective is observed as one lexical item, i.e. чёрно-белый
(black-and-white), тёмно-синий (dark blue), нежно-голубой (light blue) tagged as one word
of color. Searching by the tag “color” in the RNC, we could find 56 of such units. In other
cases, such compound adjectives are tagged as two separate lexical units, e.g. ярко-красный
(bright red) is tagged as two lexical items яркий (bright) and красный (red). Using the
random review, we could make a list (and probably not a full one) of 740 adjectives of color
tagged as two words with separate descriptions.
5 Research Technique
Step 1. Within the poetic corpus of the RNC we have created 25 sub-corpora, each of
which covered 10 years in accordance to the time of writing the texts. The whole set covered
the period from 1750 to 1999.
In every sub-corpus we made a search of adjectives by the semantic tag “color” (Fig. 3).
In each set of search results, we fixed the following data:
the volume of the user sub-corpus (the total number of words);
the number of texts (poems) which include tagged adjectives;
the number of tagged adjectives;
the list of lemmas on every page of the search results (Table 2).
Lists of lemmas from all search result pages were copied to a united MS Excel table sheet
(Fig. 4).
By functions of MS Excel, the summary table of value was formed, in which the sum of
uses (i.e. the sum of page-to-page results is made) of each lemma was counted (Fig. 5).
To the table got from the summary table the IPM column was added. The rate of IPM is
counted using the formula b/c × 1000000 where b is the number of uses of a lexeme and c is the
volume (the quantity of words) of the sub-corpus (for given in the corpus of texts from 1900
to 1909). The formula was inserted in the string of formulas, with the results generated into
9
Figure 4: A fragment of the overall list of page-to-page lemma lists of search results for the
tag “color” within the 1900–1909 sub-corpus
Figure 5: A fragment of the summary table of value with the sum of use frequency for each
lemma for the period 1900–1909
the table cells automatically. The procedure is finished with inserting the “period” column
Table 2: A fragment of the list of lemmas on the page with search results from the RNC
Lemma Number of word uses
белый / white 19
черный / black 14
желтый / yellow 12
темный / dark 10
светлый / light 9
красный / red 6
... ...
10
Figure 6: A fragment of the frequency table for color adjectives in the sub-corpus of texts
written from 1900 to 1909
containing the signification of the observed lapse of time (Fig. 6).
Describing the first step, we should notice that there is a certain difficulty when defining
the sub-corpus, as the creation dates of many texts are not exactly defined and shown as the
time lapse. If the time lapse of a text creation is defined, say, as 1909–1911, it can be included
both in the massive of 1900’s and in the massive of the 1910’s. We cannot be sure about the
quantity of lexical units within our sets, which are reflected twice, but we hope that it had no
significant influence on results.
Step 2. 25 tables created at the first stage (according to the number of decades) are
united into an overall MS Excel table. In our case, the overall table (Fig. 7) contains 2,004
rows (excluding the headline). The data from the table may be sorted by one or several
criteria. We can also build the pivot diagrams basing on the table, thus, some regularities of
frequency behavior of certain lexical units can be observed.
Then, to every row of the overall table we added the letter code of the class from the
classification described above (Fig. 8). The meanings of the codes are as follows: АН – analog
adjectives, Дв – double adjectives, Дм – diminutives, Кв – quasi-colors, Пд – adjectives of
likeness, Пр – object-related adjectives, СЦ – adjectives of color per se. We simplified a bit
the routine work of manual inserting the codes, but we do not describe here the details.
Step 3. From the table (Fig. 8), we generated a pivot diagram, with period as filter,
classes and lemmas (order of terms is of importance!) as categories and IPM as values (Fig. 9).
MS Excel allows the setting of the different parameters for getting the final data. For
example, we can set and get the diagram of use of adjectives of all classes during the whole
studied time lapse (1750–1999).
It is obvious that some data are incomparably smaller than others, so the corresponding
elements of the diagram would not be seen. In such cases, two methods can be used, depending
11
on the problems. Firstly, we can make a logarithmic scale on the vertical axis (Fig. 10).
Secondly, we can create the diagrams selecting the comparable data.
6 Results of the Research
6.1 Frequency Behavior of The Color Adjectives with the Time Lapse
We can see from the diagram (Fig. 10) that, despite the uncomfortable scale due to the
big data, the quantitative difference in coloratives of different classes is obvious. Most of the
lexical units are double adjectives.
According to the diagram, the number of lexical units for analog adjectives is less than for
all others, but it is obvious that it is because only about one-third of the coloratives belonging
to such class have signs of semantic annotation.
At the same time, such value as IPM doesn’t depend on the consistency of assignment
of tags or the bigger or smaller number of lexical units. Therefore, the diagram shows the
objective value of the frequency behavior of words. The adjectives meaning the color per
se have the highest IPM. The IPM of quasi-color and analog adjectives is also quite high
(Fig. 11). From this diagram (Fig. 11), we can see the proportions between the frequency of
adjectives of color of three most frequent classes. The adjectives of other classes have much
lower frequency, incomparable to these three classes.
Comparing the IPM change dynamics for coloratives from different classes within the
time lapse (Fig. 12), we can see very interesting tendencies. In the 18th century, the analog
adjectives were the most frequent. In the 19th century. the quasi-colors became the most
Figure 7: A fragment of the overall table, sorted by period (ascending) and IPM (descending)
12
Figure 8: Fragment of the overall table with the added column “class” (sorted by lemmas)
Figure 9: Building of the pivot diagram for changing of the IPM dynamics by classes during
the certain time lapse (function of MS Excel)
frequent ones. In the 20th century, the adjectives of the color per se had the highest IPM. We
can also notice that the highest rates of the adjectives of quasi-colors and colors per se are
13
Figure 10: The pivot diagram of use of the adjectives of for whole period (1750–1999) and all
classes with the logarithmic scale on the vertical axis (IPM)
Figure 11: A diagram for analogs, quasi-colors and colors per se with the absolute scale on
the vertical axis (IPM)
identified during the last decade of the 19th century (quasi-colors) and the first decade of the
20th century (colors per se). After that, the IPM for both classes was falling, much more for
quasi-color adjectives until the middle of the century.
Figure 12: The dynamics of the IPM change for classes of color adjectives within 1750 to
1999 period
We’ll now construct diagrams that allow us to see the frequency behavior of lexemes
of adjectives over time periods of 18th , 19th and 20th centuries, inside three classes of most
frequent color adjectives: analog adjectives, quasi-colors, and adjectives per se.
14
Figure 13: IPM of adjectives with the tag “color”, sorted by class and then by descending
IPM for 1750–1799
The diagram shows that the high frequency of analog adjectives is mainly due to the high
frequency of the adjective златой, which is the archaic form of the word золотой (golden).
The IPM of this lexical unit is more than twice the IPM of the most frequent adjectives
belonging to the different classes: adjective colors per se and quasi-colors. Apparently, the
point is the polysemy of this word. Besides the meaning of color, it, as is characteristic of
an analog adjective, means made of gold, but also has a very common metaphorical meaning
‘happy’. It is important to note that the maximal IPM in this chronological period is close to
400 – the adjective златой (Fig. 13).
Figure 14: IPM of adjectives with the tag “color”, sorted by class and then by descending
IPM for 1800–1899 period
In the 19th century, as can be seen in Fig. 14, the rise of IPM is observed in quasi-colors,
with the three most frequent lexemes of this class being adjectives светлый (light), темный
(dark) and бледный (pale). Within the class of colors per se, the most frequent lexemes
are черный (black) and белый (white), which corresponds to the conclusions of our previous
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publications [Masevich, Zakharov, 2019]. The highest IPM in this period is close to 700 – the
adjective cветлый (light).
Figure 15: IPM of adjectives with the tag “color”, sorted by class and then by descending
IPM for 1900–1999 period
As one can see in Fig.15, the most frequent in the poetic texts of the 20th century are the
adjectives of color per se. Moreover, the highest values of IPM are observed in the lexemes
белый (white) and чeрный (black), i.e. achromatic colors. Note that the IPM value of the
adjective темный (dark) is higher than that of the adjectives красный (red), синий (blue),
зеленый (green), голубой (light blue), and the IPM of светлый (light) is approximately equal
to the average of these four words. The highest IPM in the texts of 20th century is close to
1200.
7 Conclusion
The adjectives that we examined in this article have one common attribute – the presence
of the color tag in the semantic annotation of RNC. According to the data obtained, the
frequency of occurrence of adjectives in a poetry corpus is higher than their frequency in the
main corpus and is noticeably higher than in oral speech or newspaper articles.
The description of the color picture of the world in Russian poetic texts is presented in
terms of lexemes and semantic classes. We have divided adjectives of colors into five groups,
which give some structure to the color world picture.
A characteristic feature of the frequency behavior of color adjectives in general is a pro-
nounced growth at the end of the 19th century and the beginning of the 20th .
In our opinion, we have been able to identify the main models of adjectives frequency
behavior in a rather significant time period. It is shown that in the second half of the 18th
century, the analog adjectives turn out to be the most frequent. It should also be noted that in
19th century poetic texts the frequency of adjectives of quasi-colors is higher than of adjectives
of color per se. In 20th century texts adjectives of color per se become most frequent.
It is noteworthy that, along with a relatively small number of the most frequent adjectives,
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there is, a very large number (nearly 800) of lexemes of complex color terms of a single use.
This structure of the lexical array resembles the frequency distribution according to Zipf’s
law.
The study reveals significant shortcomings of RNC semantic annotation, its inconsistency,
which does not allow to take into account the real number of lexemes of some adjective types,
in particular those that we name analog, quasi-color, as well as double adjectives.
At the next stage of our work we plan to supplement our studies with a detailed analysis
of the behavior of low-frequency adjectives classes, as well as some data on the use of color
terms in the vocabularies of individual authors.
As a rule, we refrain from interpreting the data obtained. Nevertheless, it seems that our
work results, in addition to the statistical data presented, could be proposed as a methodolog-
ical approach to research based on a linguistic corpus.
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