A Corpus-Linguistic Approach to the colour naming debate
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A Corpus-Linguistic Approach to the colour naming debate
Luigi SQUILLANTE
Sapienza – Università di Roma (Italy)
Stiftung Universität Hildesheim (Germany)
Abstract
Since the publication of the famous work by Berlin & Kay (1969) on universality and evolution of
basic colour terms, several studies on colours have tried to support or reject the hypothesis that
language is shaped by universals of perception. In fact, colours are a privileged subject in this issue
because of their twofold nature of both results of biological perceptions and lexical items of language.
This work presents a new contribution to the so called “colour naming debate” from a point of view
that, as far as we know, has not been taken yet into account in studies on this subject: the
phraseological perspective. Our analysis, carried out on a large set of Italian nominal multiword
expressions (MWEs) including colour terms, shows that colours are not equally distributed in this kind
of expressions and, if only idiomatic MWEs are considered, the order arising from the quantitative
distribution of colour terms strongly reproduces Berlin & Kay's hierarchy. In this sense our results
support the idea that perception can influence language and show how also phraseological and corpus-
based studies can shed new light on this subject.
1. Introduction
It is well known that different languages develop different lexical categories in order to refer to certain
areas of the perceivable spectrum that are commonly called colours. The focus on the difference
between the terms used to refer to colours in all the languages is not new to linguistic studies and can
be brought back to the nineteenth century, when works like those by Gladstone (1858), Allen (1879)
and Geiger (1880) first appeared. The initial debate focused on whether it was possible to infer an
evolution in the human perception of hues from a philological analysis of the usage of colour terms in
the human literature, from the ancient epic poems to nowadays. The answer to such a question was
soon provided by the study of Magnus (1880), showing that lexical distinctions or the lack of terms for
expressing certain colours did not seem to imply different or deficit perceptions in the speakers: the fact
that some population had just one term to identify a hue, which another population was used to refer to
by means of two or more words, was rather due to different ways of categorizing the same physical
reality1.
The investigation on the correlation between colour terms and perception was pursued by several
studies during the twentieth century (Ray 1952, 1953; Conklin 1955; Nida 1959 among the others), all
of which insisted on the arbitrary possibility of languages of segmenting the spectral continuum.
However, it was not until the publication of the famous work by Berlin & Kay (1969) on universality
and evolution of basic colour terms that this topic started to gain much attention from the scientific
community, leading to what today can be defined as the "colour naming debate", spreading throughout
the fields of anthropology, cognitive sciences, linguistics and philosophy.
The great importance of Berlin & Kay's work is that it represents one of the most influential criticisms
to the Sapir-Whorf hypothesis (e.g. Sapir, 1921:219; Whorf 1956 [1940]:212) and to linguistic
relativism, which, during the first half of the twentieth century, had broadly dominated the research
1 This concept will be fully developed in general terms some years later by Saussure, with the notion of arbitrarity of
signs. In the structuralist frame, Hjemslev (1968[1943]:57-58) will explicitly take the lack of biunivocal corrispondence
between colour terms in English and Welsh as an example to show how languages choose arbitrarly how to categorise
the same physical entities.approaches to social sciences. As Kay and Maffi (1999:744) underline, "with the ascendance in the
1920s, '30s and '40s of linguistic and cultural relativity [...] color came to be singled out as the parade
example of a lexical domain in which the control of language over perception is patent [...]". Against
this interpretation, according to which language shapes perception, Berlin & Kay proposed a
universalistic point of view, holding that language is shaped by universals of perception. Their
experiment on 98 different languages showed that there exists a simple rule defining a universal
hierarchy for the appearance of the basic color terms in every language:
[white, black] < red < [green, yellow] < blue < brown < [purple, pink, orange, grey]2.
The hierarchy is developed on the base of the presence of the colour terms within the analyzed
languages. In fact, according to the order above, it was seen that if a language has one term, then all the
terms on its left are attested in the same language. The eleven colours included in the hierarchy were
chosen according to several principles (Berlin & Kay, 1969:6) intended to define what an ideal basic
colour term is3.
The results of Berlin & Kay's experiment were discussed and tested in the ensuing years, leading to a
strong polarization between universalists and relativists. As Kay & Maffi (ibid.) recall, psychologists
tended to welcome and support by new empirical testings Berlin & Kay's findings (among the others
Bronstein 1973a,b; Brown 1976, Collier 1976; Shepard 1992), while anthropologists raised doubts on
methodological issues (e.g. Hickerson, 1971; Durbin, 1972; Collier 1973; Conklin 1973), especially
those regarding the concept of "basic colour term" (Saunders 1995, 1997; Lucy 1996).
However, empirical and theoretical considerations raised by the following studies produced several
modification of the universal model originally proposed in 1969 (Kay and McDaniel, 1978; Kay and
Maffi, 1999, among the others) which, although not invalidating the hierarchy shown above, led to
different interpretations of the mechanisms governing the evolution of the appearance of colour terms.
One of the main issue concerned the fact that "the evolutionary sequence that views the development of
basic color-term lexicon [is] not [seen] as the successive encoding of foci, but as the successive
differentiation of previously existing basic color categories 4" (McDaniel, 1974, recalled in Kay and
McDaniel, 1978:640). Moreover "the Kay and McDaniel model emphasizes [...] the six primary colors
of opponent theory (black, white, red, yellow, green, blue)" (Kay and Maffi, 1999:745).
Although the colour naming debate is still active nowadays, in recent years the availability of new data
and the evolution of complex numerical algorithms have shown new ways of testing the hypothesis,
confirming the existence of universality in color naming systems (e.g. Baronchelli et al., 2010).
2. Colour terms and Multiword Expressions
In 1879, when the colour naming debate was just at the beginning, Allen replied to Gladstone's
opinions on the ambiguity of the use of colour terms in Homer's poems in these terms:
"Mr. Gladstone tells us that they [the Homeric Greeks] could not have understood real
colour by their apparent colour terms, because the words are used so loosely. Here,
2 Here "green means green: there, it means fresh or young. [...] Do Englishmen never talk of
green old age or Americans of green corn, which is really pale yellow? Is not red blood
confronted with sangre azul and red wine with petit vin bleu? [...] In short, are not
colour terms always vague, and are they not vaguer in the idealized language of poetry
than anywhere else?" (Allen, 1879:267, cited in Berlin & Kay, 1969:137, italics added
by the author).
Although Allen's point was just to argue that one can not abstract the concept of colours from their
figurative and sometimes very vague meanings (especially in poetry), all his examples mention colour
terms appearing in what nowadays are generally referred to as multiword expressions (hereafter
MWEs).
MWEs are phenomena of preeminent interest in phraseology and contemporary corpus linguistics.
They include a great variety of entities lying on a continuum between lexicon and syntax, whose
typical features include morpho-syntactic fixedness, semantic restrictions, semantic unpredictability,
non-grammatical constructions, conventionality and institutionalization. Their interpretation generally
crosses the boundaries between words (Sag et al., 2002) and one of the most useful definition, able to
comprehend a great number of phenomena, is that proposed by Calzolari, Fillmore et al. (2002:1934),
according to whom a MWE is "a sequence of words that acts as a single unit at some level of linguistic
analysis".
The phenomenon of MWEs has been long studied in the linguistic tradition because of its relevance to
every language. Despite their apparent anomalous behavior, MWEs are "an important and frequent
phenomenon in human language" (Ramisch et al., 2010:1), as Sinclair (1991) definitely attested by the
formulation of his famous idiom principle, which stated that idiomatic and morpho-syntactically
restricted constructions are as normal and natural in discourse as free combinations.
Apart from the great amount of theoretical works on MWEs developed within the major linguistic
frameworks throughout the twentieth century, in recent years the computational approach to this
phenomenon has become one of the dominant lines of research in this field. In fact, although none of
the features cited above appears as a necessary and sufficient condition to attest the presence of a
MWE, the components of this kind of entities exhibit a strong tendency to co-occur in texts more
frequently than they separately appear with other words. This led to the development of several statistic
approaches and association measures in order to identify, study and automatically extract MWEs from
texts (just to mention some: Evert and Krenn, 2001; Evert, 2004; Kilgarriff, 2006; Ramisch et al.,
2010).
The great amount of structured textual data, available nowadays in large corpora, allows researchers to
deepen the studies on MWEs in new testable and empirical ways. For example, starting from Allen's
considerations cited above, one can study the role of colour terms appearing as components of MWEs.
The present work, in fact, is intended to contribute to the colour naming debate from the phraseological
point of view that, as far as we know, has not been examined yet in the studies on this subject. Since
MWEs represent a very important linguistic phenomenon and colour terms can occur in such entities as
part of the lexicon, it is reasonable to establish if there are preferences in the choice of colours in the
creation of this kind of expressions.
The study exposed below is focused on the Italian language, although it has potentially relevant cross-
linguistic implications.
3. Data and Methodology
In our work we consider the Italian equivalents of the eleven basic colour terms of Berlin & Kay's
hierarchy (bianco, eng. white; nero, eng. black; rosso, eng. red; verde, eng. green; giallo, eng. yellow;
blu, eng. blue; marrone, eng. brown; viola, eng. purple; rosa, eng. pink; arancione, eng. orange; grigio,
eng. grey), plus the colour azzurro (eng. light blue) which in Italian, as well as in other non-Germaniclanguages such as Russian or Turkish, is considered to be distinct from blue (Philip, 2003:12). In
addition violetto and arancio (two variants of purple and orange) are also taken into account for their
potential competitiveness with their synonymicous forms.
Our first reference is GRADIT (1999-2007), the most comprehensive lexicographic resource for the
Italian language. This dictionary includes about 130.000 MWEs that have been selected according to
one or more of the following criteria (De Mauro, 2005:88-89):
- the existence of an unpredictable semantic addition to the meanings of the component words;
- syntactic and/or lexical fixedness with respect to lexical or structural variations that would
result in the loss of the idiomaticity of the expression;
- significant presence in a specialized language, where MWEs typically form terminology.
These criteria are able to include both typically figurative expressions generally called idioms and
expressions in which the components are interpreted according to one of their basic sense, with no
further unpredictable semantic addition (especially in the case of terminology). At the same time, this
definition do not consider more flexible expressions like collocations, since GRADIT has not been
developed as a combinatory dictionary.
We start by extracting from GRADIT all the nominal MWEs in which a colour term appears as an
adjective modifying the nominal head, obtaining a list of 943 entities (such as "scatola nera", eng.
black box).
In order to have a more complete resource to work on, we also consider MWEs from the Italian corpus
PAISÀ (2012). The PAISÀ corpus is a large resource for the Italian language, composed of ca. 380.000
documents and 250 million tokens. It collects different types of texts extracted automatically from the
web on the base of word pairs elicited from GRADIT and used as a seed list. Since it is morpho-
syntactically annotated, it allows for queries of combinations of part-of-speech categories.
In order to consider MWE candidates we use several scripts of the computational tool mwetoolkit
(Ramisch et al., 2010) to extract noun + adjective 5 combinations, using five statistical association
measures provided by the tool (maximum likelihood estimator, pointwise mutual information, log-
likelihood, Dice's coefficient, Student's t-score).
Once we have sorted the candidates according to their scores, we consider only those containing
the colour terms we analyze as adjectives (thus appearing as the second component of the candidates)
and which appear among the top-500.000 candidates for each of the five association measures. Then
we filter out manually false positives that do not satisfy the requirements of our definition, as well as
MWEs also attested in GRADIT. At the end of this process we retrieve 99 new MWEs from PAISÀ,
such that our material reaches a total amount of 1042 entities. This set of over 1000 MWE types
provides wide coverage and can be considered a reliable and relatively complete set to represent our
phenomenon.
4. First results
Our analysis showed the following frequency order for the colour terms used in Italian nominal MWEs:
bianco (white, ~22,2%), nero (black, ~20,6%), rosso (red, ~18,5%), verde (green, ~10,3%), giallo
(yellow, 9,5%), grigio (grey, ~5,8%), azzurro (light blue, ~4,5%), blu (blue, ~3,7%), rosa (pink,
~3,4%), violetto (purple, ~1,1%), marrone (brown, ~0,3%), arancione (orange, ~0,2%), viola (purple,
0,1%), arancio (orange, 0%), as shown in details by Table 1.
It is evident that only for the five most frequent colours the universal hierarchy is reproduced in an
exact way, while for the remaining terms there is no evidence for a correspondence with the hierarchy6.
5 [noun + adjective] is the general structure for the unmarked noun phrase in Italian.
6 It is interesting to note that combining the occurrences of the MWE types including blu and azzurro in a unique set
(labeled as blue), we retrieve the universal hierarchy order up to the sixth colour. However, since our study focuses onColour term # of MWEs % of MWEs Examples
Bianco 231 22,17 abete bianco, bandiera bianca, camice bianco
Nero 215 20,63 caffè nero, cintura nera, lavoro nero
Rosso 193 18,52 falco rosso, filo rosso, globulo rosso
Verde 107 10,27 anni verdi, pollice verde, tavolo verde
Giallo 99 9,50 bocca gialla, fiamme gialle, melone giallo
Grigio 60 5,76 corpo grigio, lupo grigio, sostanza grigia
Azzurro 47 4,51 alga azzurra, pesce azzurro, telefono azzurro
Blu 38 3,65 auto blu, fifa blu, sangue blu
Rosa 35 3,36 cronaca rosa, fiocco rosa, quarzo rosa
Violetto 11 1,06 camaleonte violetto, tartufo violetto
Marrone 3 0,29 cintura marrone, lemure marrone
Arancione 2 0,19 bandiera arancione, contrassegno arancione
Viola 1 0,10 gallinella viola americana
Arancio 0 0,00 /
Totale 1042 100,00
Table 1: Distribution of colour terms into Italian nominal MWEs.
Colour term PAISÀ ItTenTen Repubblica
Bianco 27.914 324.273 51.512
Nero 26.836 339.472 71.154
Rosso 20.156 230.685 39.051
Blu 8.046 90.023 16.938
Verde 6.749 171.839 24.890
Giallo 6.425 70.434 14.622
Azzurro 6.392 86.214 18.555
Grigio 3.780 2.399 10.258
Viola 2.635 17.257 2.373
Rosa 1.797 74.392 10.876
Arancione 1.681 12.103 1.003
Marrone 1.174* 11.547* 1.088*
Arancio 268* 3.689* 65*
Violetto 181 3.121 414
Table 2: Number of occurrences of basic colour terms (tagged as adjectives)
in three Italian corpora. The occurrences marked with an asterisk are just a
projection based on a set of 100 manually processed random samples. In
fact, “marrone” and “arancio” were tagged only as nouns in the three
corpora, although they obviously appear as adjectives as well.
the salience of the colour terms used to refer to hues and not on the hues themselves, this result is not of preeminent
interest.This result could already be of some interest, showing that the most cognitively salient colours of
Berlin & Kay's experiment seem to be preferred also in the choice of constructing MWEs. In this way
perception seems to influence the speakers' choices in creating new expressions that become
particularly sedimented in language. However a consideration must be done about the fact that the
result shown above could also be due to the frequency distribution in the general use of colour terms in
Italian. Chiari (2012), on the base of empirical evidences, suggests that Zipf's law on the relation
between frequency and meanings of a word7 (Zipf, 1949) can be expanded to MWEs as well, in the
sense that there exists a proportional relation between the frequency of a word and the number of
MWEs it forms. Nevertheless, Chiari's empirical observation considers such relation only for the
nominal heads of MWEs, while in our work colour terms only appear as modifiers.
In order to shed light on such a question, the number of occurrences of every basic colour term in
several Italian corpora are considered. Apart from the PAISÀ corpus itself, the ItTenTen (2010) and the
Repubblica (2004) corpora were chosen to perform the check. The first of the two new resources is
built by processes of web crawling, comprises about 3.1 billion tokens and it is available inside the
Sketch Engine interface (Kilgarriff et al., 2004). The second corpus is a collection of articles from one
of the major Italian newspaper, and includes about 380 million tokens. Both corpora are morpho-
syntactically annotated so that queries with POS-categories are allowed.
We choose to search only for the occurrences of the basic colour terms classified as adjectives. The
results are shown in Table 2.
Table 3 shows the different orders for the basic colour terms based on the number of occurrences found
in each of the analyzed corpora. The adaptation of Zipf's law to MWEs seems more or less supported
MWE frequency order PAISÀ ItTenTen Repubblica
Bianco Bianco Nero Nero
Nero Nero Bianco Bianco
Rosso Rosso Rosso Rosso
Verde Blu Verde Verde
Giallo Verde Blu Azzurro
Grigio Giallo Azzurro Blu
Azzurro Azzurro Rosa Giallo
Blu Grigio Giallo Rosa
Rosa Viola Viola Grigio
Violetto Rosa Arancione Viola
Marrone Arancione Marrone Marrone
Arancione Marrone Arancio Arancione
Viola Arancio Violetto Violetto
Arancio Violetto Grigio Arancio
Table 3: Comparison between the frequency order for basic colour terms
found from MWEs and in three Italian corpora.
7 Zipf's law about frequency/meanings relation states that words occurring with higher frequencies are more generic and
thus have a higher number of meanings (senses) with respect to less frequent ones.for the first three colours8; verde appears as the fourth term in two of the three corpora, according to the
MWE order, while the remaining terms do not show any significant correspondence.
On one hand, then, the order obtained from MWE type frequencies seems to be relevant independently
from the adapted Zipf's law only from giallo on; on the other hand the lower part of the chart doesn't
show a correspondence with the universal hierarchy. It is also possible that, for the more frequent
colour terms, both universals of perception and the Zipf's law act in order to make them appear in a
great number of MWEs, but at this point we are not able to discriminate between the two causes.
5. Colour terms and idioms
An interesting result, however, comes out if we take into account only those MWEs that can be
classified as idioms, that is when one of the components is used in a metaphorical or metonymical way
or when the global meaning includes an unpredictable semantic addition. In this way most of the
terminology of specialized languages is ruled out (e.g. "alga rossa", eng. red algae; "abete bianco",
eng. silver fir) because in these cases colours are mostly used just to denote the actual hue they refer to.
Such process of filtering only saves 295 of the original 1024 MWEs (see annex below), producing the
following distributions of colour terms: nero (~25.1%), bianco (20%), rosso (~16.6%), verde (~10.5%),
giallo (~7.1%), blu (~6.4%), azzurro (~5.4%), rosa (~5.1%), grigio (~2.7%), arancione (~0.7%),
marrone (~0.3%), arancio (0%), viola (0%), violetto (0%). The results are shown in details in Table 4.
It is possible to note two things: (i) this time the universal hierarchy is reproduced in an exact way,
except for marrone which seems to be shifted in the last group of the hierarchy; (ii) azzurro can also be
included in the last group.
Colour term # of MWEs % of MWEs Examples
Nero 74 25,08 aristocrazia nera, pecora nera, uomo nero
Bianco 59 20,00 acque bianche, calore bianco, morte bianca
Rosso 49 16,61 basco rosso, bollino rosso, croce rossa
Verde 31 10,51 carta verde, onda verde, numero verde
Giallo 21 7,12 febbre gialla, pagine gialle, romanzo giallo
Blu 19 6,44 banana blu, caschi blu, colletto blu
Azzurro 16 5,42 arma azzurra, parco azzurro, pesce azzurro
Rosa 15 5,08 balletto rosa, foglio rosa, punto rosa
Grigio 8 2,71 corpo grigio, eminenza grigia, materia grigia
Arancione 2 0,68 bandiera arancione, contrassegno arancione
Marrone 1 0,34 cintura marrone
Arancio 0 0,00 /
Viola 0 0,00 /
Violetto 0 0,00 /
Totale 295 100,00
Table 4: Distribution of colour terms into Italian nominal idioms.
8 Although the order of bianco and nero seems unstable, Table 2 shows that their number of occurrences is very close in
terms of magnitude. The occurrences for red, instead, appear quite well separated from the first two terms.With regard to observation (i), the possibility of downgrading the brown colour term is not so problematic since already Kay & McDaniel's modification of the original universal model underlined the preeminence of only white, black, red, green, yellow and blue as fundamental categories in the partition of the perceivable spectrum. On the other hand, conclusion (ii) is fully reasonable and in some way expected: first, azzurro is a derivation from blue in the same way as pink derives from red, and one expects the recourse to such terms, which are just subtle hues of more definite and fundamental colours, after that these are already developed and available; secondly, and most important, azzurro can be seen as one more basic term that a language like English has not developed yet: in this way, if the English-based hierarchy is valid, it can not appear anywhere else but in the last group. The choice of considering idioms only is grounded in studies related to the cognitive theory of metaphor (e.g. Lakoff & Johnson, 1980), as the work by Casadei (1996) on the Italian language, which leads back the interpretation of both idioms and metaphors to cognitive schemes and physical/perceptual experiences. One of the underlying hypotheses in such works is that idioms (and metaphors, in general) can be seen as the result of the speaker's need for expressing abstract concepts in terms of concrete and perceivable elements that can be related to our senses. In our case, we can add that idioms may represent one of the phenomena in language that reflect unsupervised and instinctive links between cognitive schemes and linguistic production in order to express, in the case of colours, more complex concepts via the sense of sight9. Starting from these considerations we can suppose that some colours are more likely to be chosen when expressing figurative meanings because of their cognitive salience in representing certain abstract states or features10. At the same time, the different levels of success for basic colours to be institutionalized into idioms can provide a proof of their different cognitive relevance regardless of the existence of any explicit metaphor based on them (which Casadei, 1996:262, is not able to recognize apart from those for white and black) or conventional figurative meanings. In order to throw light on this point we consider the figurative meanings for each basic colour term that are attested in GRADIT. Table 5 shows the number of meanings concerning colour adjectives that do not refer to the primary denotative meaning indicating their prototypical hue 11. It is possible to see that there is no correlation between the number of institutionalized conventional meanings for the colours and the number of idioms produced. The case of blu is the most interesting: although there seems to be no figurative meaning conventionally associated with this colour, it appears anyway in the sixth rank of the chart in Table 4 with 19 idiomatic MWEs. In this sense, also the hypothesis that the number of idioms including a specific colour can depend on how many conventional figurative meanings are associated with the colour is falsified. Thus, the fact that the frequency order of the idiom types follows Berlin & Kay's hierarchy can provide further support to the univeralistic hypothesis. 6. Conclusion and future works This work has presented a new contribution to the colour naming debate from the phraseological perspective. The analysis on the presence of basic colour terms in Italian nominal MWEs has shown that colour terms are not equally distributed in this kind of expressions, and the frequency ordering of idiomatic MWEs strongly reproduces the order of the universal hierarchy first proposed by Berlin & 9 In this sense it is useful to consider the example of foglio rosa (lit. pink sheet) which is an Italian certificate printed on pink paper that allows to practise driving a car before obtaining a driving license. The institutionalization of such expression shows how speakers tend to express the abstract meaning of the document with a reference to its colour: a feature directly connected to the sense of sight. 10 With regard to the cognitive status of the opposition of black and white Casadei (1996:264) points out Lakoff's opinion, according to which white is related to positive meanings and black to negative ones because of the intuitive experience that darkness (black) implies danger, while light (white) is connected to visibility and safety. 11 To be more explicit, in the case of bianco, for example, the meaning considered are: (i) pale, (ii) clear, (iii) covered by snow, (iv) typical of europoid races (in the case of skin), (v) clean, (vi) pure, (vii) blank, (viii) typical of a christian association, (ix) typical of antirivolutionary movements; while the excluded meaning is "of the colour of snow or milk".
Colour term # of figurative meanings (adj.)
Nero 12
Bianco 9
Verde 6
Grigio 5
Rosso 4
Giallo 3
Arancione 2
Azzurro 2
Rosa 1
Arancio 0
Blu 0
Marrone 0
Viola 0
Violetto 0
Table 5: Number of figurative meanings attested in GRADIT
for the Italian basic colour terms.
Kay. In general we can attest that the distribution of colour terms in Italian idioms cannot be
exhaustively explained neither on the base of the Zipf's law on frequency and meanings adapted to
MWEs, nor by the consideration that a higher number of conventional figurative meanings for some
colour imply a higher number of idioms with the same colour. The fact that the order arising from the
distribution of colour terms into idioms follows Berlin & Kay's hierarchy can thus be an additional
proof to their conclusions.
The idea that colour terms appear into nominal idioms according to the universal hierarchy can also
suggest that perceptual preferences related to colours can be seen not only in an evolutionary or cross-
linguistic perspective, but also in the linguistic uses within the same language.
Finally this analysis has shown how also phraseology and corpus-based studies can shed new light on
the subject.
Future works on this line of research can include the extension of the set of MWEs to those including
basic colour terms as the nominal head (e.g. “azzurro cielo”, eng. sky blue) or to other grammatical
categories such as verbal or adverbial MWEs (e.g. "vedere rosso", eng. to see red; "essere al verde", lit.
"to be at green" meaning to be without any money; "di punto in bianco", lit. "of point in white"
meaning all at once). Moreover it is desirable to compare this kind of results in a cross-linguistic
perspective, especially between unrelated languages.
Annex – List of idioms by colour term
Nero: abito nero, acque nere, Africa nera, angelo nero, anima nera, aristocrazia nera, bandiera nera,
basco nero, bestia nera, borsa nera, borsaro nero, brigate nere, buco nero, caffè nero, camicia nera,
carne nera, cintura nera, continente nero, corpo nero, cravatta nera, cronaca nera, effluente nero,
eversione nera, febbre nera, fiamme nere, fumarola nera, fumata nera, gabinetto nero, giacca nera,
giornata nera, giovedì nero, goccia nera, guelfo nero, humor nero, irraggiamento nero, lavoro nero,
libro nero, lista nera, luce nera, magia nera, maglia nera, male nero, maniera nera, mano nera,marciume nero, marea nera, mercato nero, messa nera, morbo nero, morte nera, musica nera, nobiltà nera, numero nero, onda nera, oro nero, pane nero, papa nero, pecora nera, peste nera, pietra nera, polvere nera, pozzo nero, punto nero, scatola nera, settembre nero, specchio nero, tavola nera, tavoletta nera, testa nera, testina nera, umore nero, umorismo nero, uomo nero, vedova nera. Bianco: acque bianche, albero bianco, arma bianca, arte bianca, bandiera bianca, bianca signora, caffè bianco, camice bianco, cappello bianco, carne bianca, Casa Bianca, circo bianco, clown bianco, colletto bianco, cravatta bianca, cronaca bianca, effluente bianco, elettrodomestici bianchi, fratello bianco, frittura bianca, fumata bianca, globulo bianco, golpe bianco, infarto bianco, libro bianco, luce bianca, lupara bianca, magia bianca, mal bianco, materia bianca, matrimonio bianco, monte bianco, morte bianca, nana bianca, nota bianca, notte bianca, omicidio bianco, pan bianco, perdite bianche, pizza bianca, risultato bianco, rumore bianco, scheda bianca, sciopero bianco, semestre bianco, serie bianca, settimana bianca, sostanza bianca, strada bianca, striscia bianca, telefoni bianchi, terrore bianco, treno bianco, tuta bianca, vedova bianca, voce bianca. Rosso: ala rossa, armata rossa, bandiera rossa, basco rosso, biennio rosso, bollino rosso, brigate rosse, brigatista rosso, calore rosso, camicia rossa, carne rossa, cartellino rosso, clausola rossa, code rosse, croce rossa, debito rosso, disco rosso, febbre rossa, fiamme rosse, filo rosso, gamba rossa, gambe rosse, gambi rossi, gigante rossa, giubba rossa, globulo rosso, guardia rossa, infarto rosso, khmer rosso, libretto rosso, linea rossa, macchia rossa, mal rosso dei suini, mezzaluna rossa, nana rossa, nonna rossa, numero rosso, papa rosso, partito rosso, passaporto rosso, perdite rosse, polpa rossa, punto rosso, serie rossa, soccorso rosso, stella rossa, tappeto rosso, telefono rosso, toghe rosse. Verde: anni verdi, archeoastronomia verde, balletto verde, basco verde, benzina verde, berretto verde, biglietto verde, bollino verde, camicia verde, carta verde, croce verde, disco verde, fiamme verdi, libro verde, lira verde, maggese verde, maglia verde, moneta verde, numero verde, onda verde, pasdaran verde, pollice verde, polmone verde, potatura verde, raggio verde, regime verde, tappeto verde, tavolo verde, treno verde, valuta verde, zona verde. Giallo: bandiera gialla, bocca gialla, cartellino giallo, febbre gialla, fiamme gialle, fumata gialla, maglia gialla, marciume giallo, morbo giallo, nana gialla, oro giallo, pagine gialle, pan giallo, pericolo giallo, pioggia gialla, romanzo giallo, signorina gialla, sindacato giallo, stampa gialla, stella gialla, striscia gialla. Blu: auto blu, bambino blu, banana blu, basco blu, bollino blu, casco blu, colletto blu, fifa blu, gigante blu, luna blu, macchia blu, morbo blu, parco blu, sangue blu, scettico blu, striscia blu, tuta blu, uomo blu, zona blu. Azzurro: arma azzurra, camicia azzurra, croce azzurra, fiamme azzurre, maglia azzurra, malattia azzurra, morbo azzurro, nastro azzurro, parco azzurro, partito azzurro, pesce azzurro, pietra azzurra, principe azzurro, sangue azzurro, signorina azzurra, telefono azzurro. Rosa: balletto rosa, bollino rosa, cartolina rosa, colletto rosa, cronaca rosa, dente rosa di Mummery, fiocco rosa, foglio rosa, maglia rosa, marciume rosa, punto rosa, quote rosa, romanzo rosa, salsa rosa, telefono rosa. Grigio: corpo grigio, eminenza grigia, lettera grigia, letteratura grigia, marciume grigio, materia grigia, mercato grigio, sostanza grigia.
Arancione: bandiera arancione, contrassegno arancione.
Marrone: cintura marrone.
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