Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 16–25,Valencia, Spain, April 3, 2017. c©2017 Association for Computational Linguistics

Dialectometric analysis of language variation in Twitter

Gonzalo DonosoIFISC (UIB-CSIC)

Palma de Mallorca, Spaingdonoso94@hotmail.com

David SanchezIFISC (UIB-CSIC)

Palma de Mallorca, Spaindavid.sanchez@uib.es

Abstract

In the last few years, microblogging plat-forms such as Twitter have given rise toa deluge of textual data that can be usedfor the analysis of informal communica-tion between millions of individuals. Inthis work, we propose an information-theoretic approach to geographic languagevariation using a corpus based on Twitter.We test our models with tens of conceptsand their associated keywords detectedin Spanish tweets geolocated in Spain.We employ dialectometric measures (co-sine similarity and Jensen-Shannon diver-gence) to quantify the linguistic distanceon the lexical level between cells createdin a uniform grid over the map. This canbe done for a single concept or in the gen-eral case taking into account an average ofthe considered variants. The latter permitsan analysis of the dialects that naturallyemerge from the data. Interestingly, ourresults reveal the existence of two dialectmacrovarieties. The first group includesa region-specific speech spoken in smalltowns and rural areas whereas the sec-ond cluster encompasses cities that tend touse a more uniform variety. Since the re-sults obtained with the two different met-rics qualitatively agree, our work suggeststhat social media corpora can be efficientlyused for dialectometric analyses.

1 Introduction

Dialects are language varieties defined acrossspace. These varieties can differ in distinct lin-guistic levels (phonetic, morphosyntactic, lex-ical), which determine a particular regionalspeech (Chambers and Trudgill, 1998). The ex-

tension and boundaries (always diffuse) of a di-alect area are obtained from the variation of oneor many features such as, e.g., the different wordalternations for a given concept. Typically, the di-alect forms plotted on a map appear as a geograph-ical continuum that gradually connects places withslightly different diatopic characteristics. A di-alectometric analysis aims at a computational ap-proach to dialect distribution, providing quantita-tive linguistic distances between locations (Seguy,1971; Goebl, 2006; Wieling and Nerbonne, 2015).

Dialectometric data is based upon a corpus thatcontains the linguistic information needed for thestatistical analysis. The traditional approach is togenerate these data from surveys and question-naires that address variable types used by a fewinformants. Upon appropriate weighting, the dis-tance metric can thus be mapped on an atlas. Inthe last few years, however, the impressive up-swing of microblogging platforms has led to ascenario in which human communication featurescan be studied without the effort that traditionalstudies usually require. Platforms such as Twitter,Flickr, Instagram or Facebook bring us the pos-sibility of investigating massive amounts of datain an automatic fashion. Furthermore, microblog-ging services provide us with real-time communi-cation among users that, importantly, tend to em-ploy an oral speech. Another difference with tradi-tional approaches is that while the latter focus onmale, rural informants, users of social platformsare likely to be young, urban people (Smith andRainie, 2010), which opens the route to novel in-vestigations on today’s usage of language. Thanksto advances in geolocation, it is now possible todirectly examine the diatopic properties of spe-cific regions. Examples of computational linguis-tic works that investigate regional variation withTwitter or Facebook corpora thus far comprise En-glish (Eisenstein et al., 2014; Doyle, 2014; Kulka-

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rni et al., 2016; Huang et al., 2016; Blodgettet al., 2016), Spanish (Goncalves and Sanchez,2014; Goncalves and Sanchez, 2016; Malmasi etal., 2016), German (Scheffler et al., 2014), Arabic(Lin et al., 2014) and Dutch (Tulkens et al., 2016).It is noticeable that many of these works combinebig data techniques with probabilistic tools or ma-chine learning strategies to unveil linguistic phe-nomena that are absent or hard to obtain from con-ventional methods (interviews, hand-crafted cor-pora, etc.).

The subject of this paper is the language varia-tion in a microblogging platform using dialectro-metric measures. In contrast to previous works,here we precisely determine the linguistic distancebetween different places by means of two metrics.Our analysis shows that the results obtained withboth metrics are compatible, which encourages fu-ture developments in the field. We illustrate ourmain findings with a careful analysis of the dialectdivision of Spanish. For definiteness, we restrictourselves to Spain but the method can be straight-forwardly applied to larger areas. We find that,due to language diversity, cities and main townshave similar linguistic distances unlike rural areas,which differ in their homogeneous forms. but ob-tained with a completely different method

2 Methods

Our corpus consists of approximately 11 milliongeotagged tweets produced in Europe in Spanishlanguage between October 2014 and June 2016.(Although we will focus on Spain, we will notconsider in this work the speech of the Canary Is-lands due to difficulties with the data extraction).The classification of tweets is accomplished byapplying the Compact Language Detector (CLD)(McCandless, 2012) to our dataset. CLD exhibitsaccurate benchmarks and is thus good for our pur-poses, although a different detector might be used(Lui and Baldwin, 2012). We have empiricallychecked that when CLD determines the languagewith a probability of at least 60% the results areextremely reliable. Therefore, we only take intoaccount those tweets for which the probabilityof being written in Spanish is greater than 0.6.Further, we remove unwanted characters, suchas hashtags or at-mentions, using Twokenize(O’Connor et al., 2010), a tokenizer designed forTwitter text in English, adapted to our goals.

We present the spatial coordinates of all tweets

Figure 1: Heatmap of Spanish tweets geolocatedin Europe. There exist 11208831 tweets arisingfrom a language detection and tokenization proce-dure. We have zoomed in those arising in Spain,Portugal and the south of France.

in figure 1 (only the south-western part of Europeis shown for clarity). As expected, most of thetweets are localized in Spain, mainly around majorcities and along main roads.

Next, we select a word list from Varilex (Ueda etal., 2015), a lexical database that contains Spanishvariation across the world. We consider 89 con-cepts expressed in different forms. Our selectioneliminates possible semantic ambiguities. Thecomplete list of keywords is included in the sup-plementary material below. For each concept, wedetermine the coordinates of the tweets in whichthe different keywords appear. From our corpus,we find that 219362 tweets include at least oneform corresponding to any of the selected con-cepts.

The pictorial representation of these concepts ismade using a shapefile of both the Iberian Penin-sula and the Balearic Islands. Then, we constructa polygon grid over the shapefile. The size ofthe cells (0.35◦ × 0.35◦) roughly corresponds to1200 km2. We locate the cell in which a given key-word matches and assign a different color to eachkeyword. We follow a majority criterion, i.e., wedepict the cell with the keyword color whose abso-lute frequency is maximum. This procedure nicelyyields a useful geographical representation of howthe different variants for a concept are distributedover the space.

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Figure 2: Spatial distribution of a few representative concepts based on the maximum absolute frequencycriterion. Each concept has a lexical variation as indicated in the figure. The concepts are: (a) cold, (b)school, (c) streetlight, (d) fans.

2.1 Language distance

The dialectometric differences are quantified be-tween regions defined with the aid of our cells.For this purpose we take into account two metrics,which we now briefly discuss.

2.1.1 Cosine similarity

This metric is a vector comparison measure. It iswidely used in text classification, information re-trieval and data mining (Murphy, 2012). Let u andv be two vectors whose components are given bythe relative frequencies of the lexical variations fora concept within a cell. Quite generally, u and vrepresent points in a high-dimensional space. Thesimilarity measure d(u, v) between these two vec-tors is related to their inner product convenientlynormalized to the product of their lengths,

d(u, v) = 1− u · v|u||v| . (1)

This expression has an easy interpretation. If bothvectors lie parallel, the direction cosine is 1 andthus the distance becomes d = 0. Since all vec-tor components in our approach are positive, theupper bound of d is 1, which is attained when thetwo vectors are maximally dissimilar.

2.1.2 Jensen-Shannon metric

This distance is a similarity measure betweenprobability density functions (Lin, 1991). It isa symmetrized version of a more general metric,the Kullback-Leibler divergence. Let P and Q betwo probability distributions. In our case, thesefunctions are built from the relative frequencies ofeach concept variation. Our frequentist approachdiffers from previous dialectometric works, whichprefer to measure distances using the Dice similar-ity coefficient or the Jaccard index (Manning andSchutze, 1999).

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The Kullback-Leibler divergence is defined as

DKL(P ||Q) =∑

i

P (i) logP (i)Q(i)

. (2)

We now symmetrize this expression and take thesquare root,

JSD(P ||Q) =

√DKL(P ||M) + DKL(Q||M)]

2,

(3)where M = (P + Q)/2. The Jensen-Shannondistance JSD(P ||Q) is indeed a metric, i.e.,it satisfies the triangle inequality. Additionally,JSD(P ||Q) fulfills the metric requirements ofnon-negativity, d(x, y) = 0 if and only if x = y(identity of indiscernibles) and symmetry (by con-struction). This distance has been employed inbioinformatics and genome comparison (Sims etal., 2009; Itzkovitz et al., 2010), social sciences(DeDeo et al., 2013) and machine learning (Good-fellow et al., 2014). To the best of our knowledge,it has not been used in studies of language varia-tion. An exception is the work of Sanders (2010),where JSD is calculated for an analysis of syn-tactic variation of Swedish. Here, we propose toapply the Jensen-Shannon metric to lexical varia-tion. Below, we demonstrate that this idea leads toquite promising results.

2.1.3 Average distanceEquations 1 and 3 give the distance between cellsA and B for a certain concept. We assign theglobal linguistic distance in terms of lexical vari-ability between two cells to the mean value

D(A, B) =∑

i di(A, B)N

, (4)

where di is the distance between cells A and B forthe i-th concept and N is the total number of con-cepts used to compute the distance. In the cosinesimilarity model, we replace di in equation 4 withequation 1 whereas in the Jensen-Shannon metricdi is given by equation 3.

3 Results and discussion

We first check the quality of our corpus with a fewselected concepts. Examples of their spatial distri-butions can be seen in figure 2. The lexical varia-tion depends on the particular concept and on thekeyword frequency. We recall that the majorityrule demands that we depict the cell with the color

Figure 3: Linguistic distances for the concept coldusing (a) cosine similarity and (b) Jensen-Shannondivergence metrics. The horizontal (vertical) axisis expressed in longitude (latitude) coordinates.

corresponding to the most popular word. Despite afew cells appearing to be blank, we have instancesin most of the map. Importantly, our results agreewith the distribution for the concept cold reportedby Goncalves and Sanchez (2014) with a differentcorpus. The north-south bipartition of the varia-tion suggested in figure 2(a) also agrees with moretraditional studies (Ordonez, 2011). As a conse-quence, these consistencies support the validity ofour data. The novelty of our approach is to furtheranalyze this dialect distribution with a quantitativemeasure as discussed below.

3.1 Single-concept case

Let us quantify the lexical difference between re-gions using the concept cold as an illustration.First, we generate a symmetric matrix of linguis-tic distances mij(d) between pairs of cells i andj with d calculated using equation (1) or equa-tion (3). Then, we find the maximum possibled value in the matrix (dmax) and select either itscorresponding imax or jmax index as the refer-ence cell. Since both metrics are symmetric, thechoice between imax and jmax should not affectthe results much (see below for a detailed anal-ysis). Next, we normalize all values to dmax andplot the distances to the reference cell using a color

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Figure 4: Linguistic distances as in figure 3 butwith a minimum threshold of 5 tweets in each cellusing (a) cosine similarity and (b) Jensen-Shannonmetric.

scale within the range [−1, 1], whose lowest andhighest values are set for convenience due to thenormalization procedure. The results are shownin figure 3. Panel (a) [(b)] is obtained with thecosine similarity (Jensen-Shannon metric). Cru-cially, we observe that both metrics give similarresults, which confirm the robustness of our di-alectometric method.

Clearly, cells with a low number of tweets willlargely contribute to fluctuations in the maps. Toavoid this noise-related effect, we impose in fig-ure 4 a minimum threshold of 5 tweets in everycell. Obviously, the number of colored cells de-creases but fluctuations become quenched at thesame time. If the threshold is increased up to10 tweets, we obtain the results plotted in fig-ure 5, where the north-south bipartition is nowbetter seen. We stress that there exist minimaldifferences between the cosine similarity and theJensen-Shannon metric models.

3.2 Global distance

Our previous analysis assessed the lexical distancefor a single concept (cold). Let us now take intoaccount all concepts and calculate the averaged

Figure 5: Linguistic distances as in figure 3 butwith a minimum threshold of 10 tweets in each cellusing (a) cosine similarity and (b) Jensen-Shannonmetric.

distances using equation (4). To do so, we pro-ceed as above and measure the distance from anyof the two cells that presents the maximal value ofd, where d is now calculated from equation 4. Asaforementioned, dmax connects two cells, whichdenote as C1 and C2. Any of these can be selectedas the reference cell from which the remaining lin-guistic distances are plotted in the map. To en-sure that we obtain the same results, we plot thedistance distribution in both directions. The re-sults with the cosine similarity model are shownin figure 6. It is worth noting that qualitatively theoverall picture is only slightly modified when thereference cell is changed from C1 [figure 6(a)] toC2 [figure 6(b)]. The same conclusion is reachedwhen the distance is calculated with the Jensen-Shannon metric model, see figures 7(a) and (b).

After averaging over all concepts, we lose infor-mation on the lexical variation that each conceptpresents but on the other hand one can now inves-tigate which regions show similar geolectal vari-ation, yielding well defined linguistic varieties.Those cells that have similar colors in either fig-ure 6 or figure 7 are expected to be ascribed to thesame dialect zone. Thus, we can distinguish twomain regions or clusters in the maps. The purple

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Figure 6: Global distances averaged over all con-cepts. Here, we use the cosine similarity measureto calculate the distance. The color distributiondisplays a small variation from (a) to (b) due tothe change of the reference cell.

background covers most of the map and representsrural regions with small, scattered population. Ouranalysis shows that this group of cells possessesmore specific words in their lexicon. In contrast,the green and yellow cells form a second clusterthat is largely concentrated on the center and alongthe coastline, which correspond to big cities andindustrialized areas. In these cells, the use of stan-dard Spanish language is widespread due proba-bly to school education, media, travelers, etc. Thecharacter of its vocabulary is more uniform ascompared with the purple group. While the pur-ple cluster prefer particular utterances, the lexiconof the urban group includes most of the keywords.Importantly, we emphasize that both distance mea-sures (cosine similarity and Jensen-Shanon) giverise to the same result, with little discrepancies onthe numerical values that are not significant. Thepresence of two Twitter superdialects (urban andrural) has been recently suggested (Goncalves andSanchez, 2014) based on a machine learning ap-proach. Here, we arrive at the same conclusionbut with a totally distinct model and corpus. Theadvantage of our proposal is that it may serve as auseful tool for dialectometric purposes.

Figure 7: Global distances averaged over all con-cepts. Here, we use the Jensen-Shannon metric tocalculate the distance. The color distribution dis-plays a small variation from (a) to (b) due to thechange of the reference cell.

4 Conclusions

To sum up, we have presented a dialectrometricanalysis of lexical variation in social media postsemploying information-theoretic measures of lan-guage distances. We have considered a grid ofcells in Spain and have calculated the linguisticdistances in terms of dialects between the differentregions. Using a Twitter corpus, we have foundthat the synchronic variation of Spanish can begrouped into two types of clusters. The first re-gion shows more lexical items and is present in bigcities. The second cluster corresponds to rural re-gions, i.e., mostly villages and less industrializedregions. Furthermore, we have checked that thedifferent metrics used here lead to similar resultsin the analysis of the lexical variation for a rep-resentative concept and provide a reasonable de-scription to language variation in Twitter.

We remark that the small amount of tweets gen-erated after matching the lexical variations of con-cepts within our automatic corpus puts a limitto the quantitative analysis, making the differ-ences between regions small. Our work might beimproved by similarly examining Spanish tweetsworldwide, specially in Latin America and the

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United States. This approach should give moreinformation on the lexical variation on the globalscale and would help linguists in their dialectalclassification work of micro- and macro-varieties.Our work hence represents a first step into theambitious task of a thorough characterization oflanguage variation using big data resources andinformation-theoretic methods.

Acknowledgments

We thank both F. Lamanna and Y. Kawasaki foruseful discussions and the anonymous reviewersfor nice suggestions. GD acknowledges supportfrom the SURF@IFISC program.

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Supplementary material

Here we provide a list of our employed conceptsand their lexical variants.

Concept Keywordsstapler abrochador, abrochadora,

clipiador, clipiadora, clip-sadera, corchetera, cosedora,engrampador, engram-padora, engrapador, en-grapadora, grapadora,ponchadora, presilladora

sidewalk acera, anden, baden, calzada,conten, escarpa, vereda

bedspread acolchado, colcha, colchon,cubrecama, cubrecamas,cubrelecho, edredon, sobre-cama

flight attendant aeromoza, azafata, hostess,stewardess

poster afiche, anuncio, cartel, car-telon, letrero, poster, pro-paganda, rotulo, tablon deanuncio

pencil sharpner afilalapices, afilalapiz, afil-aminas, maquineta, saca-punta, sacapuntas, tajador,tajalapices, tajalapiz

bra ajustador, ajustadores,brasiel, brassiere, corpino,portaseno, sosten, soutien,sutien, sujetador, tallador

swimming pool alberca, pileta, piscina

Concept Keywordselevator ascensor, elevadorglasses anteojos, espejuelos, gafas,

gafotas, lentespopcorn alepa, cabritas de maız,

canchita, canguil, co-caleca, cotufas, crispetas,crispetos, maız pira, palomi-tas, pipocas, pochocle,pochoclo, pocorn, popcorn,poporopo, pororo, rosita demaız, tostones

sandals alpargata, chanclas, chan-cletas, chinelas, cholas,cutalas, cutaras, pantuflas,sandalias, zapatillas

aluminum paper alusa-foil, foil, papel albal,albal, papel reinolds, pa-pel aluminio, papel de alu-minio, papel de estano, pa-pel de plata, papel encerado,papel estanado, papel paracocinar, papel platina

store window aparador, escaparate,mostrador, vidriera, vitrina

coat hanger armador, cercha, colgador,gancho de ropa, percha,perchero

headphones audıfonos, auriculares, cas-cos, casquitos, headphones,hedfons, talquis

car auto, automovil, carro,coche, concho, movi

bus autobus, autocar, bus,camioneta, guagua, mi-crobus, omnibus, taxibus

jeans azulon, azulones, bluejean, bluyın, blue jeans,bluyıns, jeans, yıns, lois,mahon, mahones, pantalonde mezclilla, pantalones demezclilla, pantalon vaquero,pantalones vaqueros, pan-talones tejanos, vaqueros,tejanos, pitusa, pitusas

backpack backpack, bolson, macuto,mochila, morral, salveque

boat barca, bote, canoa, cayuco,chalana, lancha, patera, yola

miss echar de menos, extranar,anorar

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Concept Keywordsfender barrero, cubrebarro, cubr-

erruedas, guardabarro,guardafango, guardalodo,guardalodos, guardapolvo,lodera, polvera, quitalodo,salpicadera, salpicadero,tapabarro

sandwich bocadillo, bocadito, bocata,emparedado, sandwich,sanguis, sanguich, sanwich

suitcase bolso de viaje, maleta, valija,veliz

boxers bombacho, boxers, calzon,calzoncillo, calzoncillos, pan-taloncillos, ropa interior, slip,trusa, taparrabos, jokey

lighter bricke, brık, chispero, en-cendedor, fosforera, lighter,laiter, mechero, yesquero,zippo

backhoe buldozer, buldocer, caterpil-lar, caterpılar, excavadora,maquina excavadora,maquina topadora, moto-pala, pala excavadora, palamecanica, retroexcavadora,topadora

pot/pan cacerola, cacico, cacillo,caldero, cazo, cazuela, olla,paila, pota, tartera, cazuela,sarten, freidera, freidero,fridera, paila

socks calcetas, calcetines, medias,soquetes

reclining chair cheilon, butaca, camastro,catre, cheslon, gandula,hamaca, perezosa, repo,reposera, silla de extension,silla de playa, silla de sol,silla plegable, silla plegadiza,silla reclinable, tumbona

living room comedor, cuarto de estar, es-tancia, lıving, livin, recibidor,sala de estar, salita de estar,salon

computer computador, computadora,microcomputador, micro-computadora, ordenador,PC

washer lavadora, lavarropa, lavar-ropas, maquina de lavar

Concept Keywordsmatchstick cerilla, cerillo, fosforoheadlight cristal de frente, cristal de-

lantero, luna delantera, lunasdelanteras, luneta, parabrisa,parabrisas, vidrio delantero,windshield

skirt enagua, falda, pollera, sayablackboard encerado, pizarra, pizarron,

tablerodish drainer escurreplatos, escurridero, es-

curridor, platera, secaplatos,secavajilla

poncho estola, jorongo, mananera,poncho, ruana

street light farol, farola, farolillo, lumi-naria, poste de luz, posteelectrico

dishwasher friegaplatos, lavadora deplatos, lavaloza, lavaplatos,lavatrastos, lavavajilla,lavavajillas, maquina de lavarplatos

refrigerator frigorıfico, heladera, hielera,nevera, refrigerador, refriger-adora

toilet paper papel confort, papel confor,papel de bano, papel de in-odoro, papel de water, pa-pel de vater, papel higienico,papel sanitario, papel toalet,rollo de papel

record player wurlitzer, burlıtser, chancha,compactera, gramola, jukebox, maquina de musica, pi-anola, rocola, tragamonedas,roconola, sinfonola, tocadis-cos, traganıquel, vellonera,vitrola

slice of cheese lamina de queso, lasca dequeso, loncha de queso, lonjade queso, rebanada de queso,rodaja de queso, slice dequeso, tajada de queso, quesode sandwich, queso en lon-chas, queso en rebanadas,queso en slice, queso ameri-cano, tranchetes

demijohn bidon, bombona, botellagrande, garrafa, garrafon,tambuche, candungo, pomoplastico

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Concept Keywordsplaster banda adhesiva, curita, es-

paradrapo, tiritaattic atico, altillo, azotea,

buhardilla, guardilla, pent-house, mansarda, tabanco

wardrobe armario, closet, placard,ropero, guardarropas

bracers breteles, bruteles, suspen-sores, tiradores, tirantes

ring anillo, argolla, aro, sortija,cintillo

tape recorder cassette, casete, grabador,grabadora, magnetofon, to-cacintas, magnetofono

merry-go-round caballitos, calesita, carrusel,tiovivo, machina

loudspeaker altavoz, altoparlante,altovoz, amplificador,megafono, parlante, mag-navoz

flower pot maceta, macetero, matera,matero, tiesto, macetera,plantera

fans aficion, aficionados,fanaticos, fanaticada,forofos, hinchada, hinchas,seguidores

waiter camarero, barman, mesero,mesonero, mozo, camarero

school colegio, escuela, centro es-colar, scuela

amusement distracciones, diversion, en-tretencion, entretenimiento,pasatiempo

stay estada, estadıa, estanciamiss equivocacion, error, falen-

cia, fallocheek cachetes, carrillos, galtas,

mejillas, mofletes, pomulomonkey chango, chimpance, macaco,

mono, mico, simio, chongomosquito cınife, mosco, mosquito,

zancudochance bicoca, chance, ocasion,

oportunidadsponsor auspiciador, auspiciante,

esponsor, patrocinador,patrocinante, propiciador,sponsor

park aparcar, estacionar, parquear

Concept Keywordsparcel encomienda, paquete postalbanana banana, banano, cambur,

guineo, platano, tombodust nube de polvo, polvadera,

polvareda, polvazal, polvero,polvoreda, polvorın, terral,terregal, tierral, tolvanera

bar bar, boliche, cantina, cerve-cerıa, pulperıa, taberna,tasca, expendio, piquera

earthquake movimiento telurico,movimiento sısmico, re-mezon, seısmo, sismo,temblor de tierra, terremoto

shooting abaleo, balacera, baleada,tiroteo

glance ojeada, miradita, vistazogreasy engrasado, grasiento, gra-

soso, mantecoso, sebosobeautiful bella, bonita, hermosa, linda,

preciosacold catarro, constipado, coriza,

gripa, gripe, resfrıo, resfri-ado, trancazo

cellophane tape celo, celofan, cinta adhesiva,cinta scotch, cintex, scotch,teip, durex, diurex, cinta pe-gante

crane grua, guinche, teclefruit cup ensalada de frutas, macedo-

nia, clerico, coctel de frutas,tuttifruti, tutifruti

gas station bomba de gasolina, bombade nafta, estacion de ser-vicio, gasolinera, bencinera,bomba de bencina, gaso-linerıa, surtidor de gasolina

interview entrevistar, reportear, inter-viuvar

obstinate cabezon, cabezudo, cabezadura, cabezota, obstinado,porfiado, terco, testarudo,tozudo

peanut cacahuate, cacahuete, manı,cacahue, cacaomani

scratch aranazo, aranon, arunetazo,arunon, rajuno, rayon,rasguno, rasgunon

sweetener edulcorante, endulzante, en-dulcina, endulzador, sacarina

thaw descongelar, deshielar

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