COPY AUTHOR'S · 2018. 8. 5. · AUTHOR'S COPY Christian F. Hempelmann* and Elisa Gironzetti An interlingual study of the lexico-semantic field LAUGH in Ken Kesey’s One Flew over

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Christian F. Hempelmann* and Elisa Gironzetti

An interlingual study of the lexico-semanticfield LAUGH in Ken Kesey’s One Flew overthe Cuckoo’s NestDOI 10.1515/jls-2015-0008

Abstract: This paper presents a series of theoretical and methodological choicesagainst previous research and innovations for practical structuralist lexical fieldstudies. Starting from a critical literature review of relevant work, it develops asynchronic interlingual study of the lexico-semantic field around words for laugh,smile, grin, etc. in an aligned corpus of eight translations of Kesey’s novel One Flewover the Cuckoo’s Nest and the English original text. Studies on paradigmatically-defined lexical fields of semantically competing words, such as the present one, havebeen criticized for not scaling beyond the actual field investigated. But with explicitmethodological decisions and for specific domains for which aligned multilingualcorpora exist they are shown to yield valuable insights. This paper reports theprogress in lexical field theory, while also reviving semantic features and applyingvisual analytics, by way of analyzing examples from an aligned literary corpus.

Keywords: lexical field theory, Kesey, laugh, smile

1 Introduction

This paper presents a methodology for semantic analysis based mainly onlexical field studies (Trier 1931; Porzig 1934; Weisgerber 1951), but augmentedby semantic features and information visualization. It illustrates its choices withselected results from a project on translations of laugh and related words inKesey’s One Flew Over the Cuckoo’s Nest (1962). There are two reasons for thisnovel to be a suitable choice for a structuralist lexical-field study of LAUGH1.

*Corresponding author: Christian F. Hempelmann, Department of Literature and Languages,Texas A&M University – Commerce, PO Box 3011, Commerce, TX 75428, USA,E-mail: [email protected] Gironzetti, Department of Literature and Languages, Texas A&M University – Commerce,PO Box 3011, Commerce, TX 75428, USA, E-mail: [email protected]

1 We’re using the upper case to refer to the whole field, including the lemma “laugh,” andparadigmatically related lemmata “smile,” “grin,” etc., in contrast to the lemma “laugh” by itself.

Journal of Literary Semantics 2015; 44(2): 141–167

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Firstly, in the novel the description of laughing and smiling behavior is used tostructure and set apart the two main characters, the protagonist McMurphy andthe antagonist Nurse Ratched (Mills 1972; Tanner 1973; Hempelmann 2007). Thisresults in a large number of tokens from the LAUGH field in the text. Secondly,the novel was so successful that it has been widely translated into manylanguages from different families. This presents us with a multilingual alignedcorpus suitable for extracting the meaning contrasts of the different lemmatathat cover the same content area within the LAUGH field (Hjelmslev 1969).

In our – traditionally structuralist – view, if a language has a paradigmaticallydefined series of words (or more precisely lemmata), each of which is a choice for thedescription of a situation, the actual choice from this series represents a fundamentalsemantic decision that is a relevant object of study independent of context (cf. Gipper1959). In our case, the LAUGH field across all languages we have studied so farprovides a series of lemmata to describe an audible and visible human behavior thatmakes fundamental distinctions about that behavior. For example, in English,wewilluse laugh to talk about a vocalized behavior, whereas when we choose grin or smile,the behavior cannot be vocalized, but must be silent, at least as far as the laughterbehavior is concerned. One can, on the other hand, audibly perform the facialgestures of grinning and smiling while talking (Quené et al. 2012). Our hypothesis isthat if we compare these LAUGH inventories across a number of languages we willdiscover a hierarchy of such fundamental distinctions as vocalized vs. non-vocalizedthat in any given languagemay only be partially lexicalized and otherwise expressedthrough context or remain implicit. For example, in Chinese, the main root xiaodominates the field, and the vocalization distinction requires additional non-corelexical material, so that wei xiao (“small xiao”) expresses a non-vocalized behaviorand xiao sheng (“xiao with voice”) describes, inter alia, a vocalized behavior.

2 Lexical semantics and lexical field theory:issues and choices

Similar to most subsequent approaches in lexical field theory2 inaugurated by it,Trier’s groundbreaking diachronic study (1931)3 on the field of VERSTAND(“mind”) in different stages of German up to the Middle High German periodis based on the structuralist notion of opposition (Saussure 1916). Trier studied

2 For the scope of our paper it is not necessary to make a terminological distinction betweenlexical fields, semantic fields, etc. because we’re working on interlingual lexical evidence torepresent semantic differences.3 For an expanded discussion including a comparison of Trier and Weissgerber, see Hoberg (1970).

142 Christian F. Hempelmann and Elisa Gironzetti

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the change over time of the meaning of words such as klug (“prudent”), weise(“wise”), gescheit (“clever”) in their relationship to each other. He limited theanalysis to semantic similarities between the target words on the paradigmaticaxis (such as synonymy, hyperonymy, antonymy), and excluded formal simila-rities (such as minimal pairhood, homophony) and syntagmatic lexical relations(later called thematic or case-roles, see Porzig 1934). For Trier, a lexical field isdefined according to the notions of totality and closure: a field is a closed totalityin which words or concepts define each other mutually (Nerlich and Clarke2000). To illustrate his idea of demarcation of one word from another he usedthe metaphor of a “mosaic” in which every word in a given field corresponds toone “tile,” its meaning being thus specified by the area and position it occupieswith respect to the other tiles of the mosaic, in particular its neighboring ones.For example, the meaning of the lexical tile glass4 in the fields of containers forliquid can be represented in structuralist terms as the conceptual area notcovered by neighboring lexical tiles, such as cup or mug.

From the work of Trier originated another important theoretical contributionto the field, namely Porzig’s (1934) essential meaning relations theory. Porzig’smain interest was to develop a semantic field theory capable of accounting alsofor the syntagmatic relations neglected by Trier, because for him the syntagmaticcombinability of a given word was relevant not only from a grammatical point ofview, but also semantically. For example, in a sentence like Will you walk ordrive home?, the verbs include information about the instrument by means ofwhich the activity of ‘going home’ is achieved, namely on foot or with a vehicle,so that walking home excludes a vehicular instrument (see also Geeraerts 2010:58). Because important aspects of a word’s meaning can remain implicit, such asthe type of locomotion in the example above, Porzig advocates a semantic fieldtheory that also accounts, within the semantic field of a word, for these syntag-matic lexical relations (or meanings) that are not explicitly expressed (see alsoCoşeriu 1977): in a lexical field structured along these lines, eat is related, forexample, to its possible agents (humans, animals), instruments (silverware,teeth, etc.), and themes (types of food).

From the brief overview of these early works in lexical field theory itbecomes clear that the main problem is the scope of the concept field. While it

4 This refers to the sense of glass as a container for liquids. The fact that words usually haveseveral senses is often not even addressed in lexical field studies, presumably under the assump-tion that if these senses are unrelated (homonymous), they will fall into different lexical fields; inany case, they are demarcated from each other. Consider, for example, ear in ear of corn vs. ear formusic. On the other hand, lexical field theory is clearly not a good approach to handle therelatedness of polysemous senses of words, for example, hand (deck) vs. hand (body part).

LAUGH in Kesey’s Cuckoo’s Nest 143

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is true that syntagmatic relationships contribute to the meaning of a givenlexical item, it is also true that a combined paradigmatic and syntagmaticperspective for lexical field theory adds difficulties to its application. For exam-ple, considering the study of LAUGH, a lexical field theory like Porzig’s wouldrequire the inclusion of the thematic role ‘instrument,’ that is to say, the meansby which the action is accomplished, because the different parts of the body,e.g., mouth, eyes, vocal folds, are implied as instruments by, for example, themeaning of laugh in contrast to smile. Accounting for all these syntagmaticrelationships in a lexical field study, while likely resulting in a more detailedanalysis, makes the definition of the field less sharp and will be left for futureextensions of the present approach.

In contrast to Porzig, as the project’s first step reported here, we are using aparadigmatic chain to define the lexical field LAUGH. That is, if speakers want todescribe facial behavior reflecting certain underlying emotions and involving,prominently, muscle movements raising the corners of the mouth and thecheeks, and wrinkling of the corners of the eyes (Action Units 12 and 6 interms of the Facial Action Coding System, cf. Ekman and Friesen 1982), possiblyaccompanied by semelfactive or iterative non-linguistic vocal behavior, in mostlanguages they must make a choice as to which word(s) to use to describe thisbehavior. In English this choice is between words such as laugh, smile, grin, butin other languages the set of choices is assumed to be different and likely not tobe parallel to the set of choices in English. In places where such non-alignmentssurface systematically, for example in translations, we expect to find semanticfeatures of that behavior that are treated differently in the source and targetlanguages. A further, – admittedly simplified – example, may serve to illustratethis notion: In German one can describe a specific type of LAUGH behavior withthe lemma gackern (“cackle,” as of a chicken), which emphasizes at least thefollowing features of the behavior in contrast to the more generic choice lachen(“laugh”): the vocal behavior is vocalized and iterative, not at low volume,certainly not silent, certainly not semelfactive as in a single ha. While speakerscan express these features in basic vocabulary as easily in German as in theclosely related Germanic language like English, they need to use additionalwords (adverbs, prepositional phrases) in other languages, like Arabic,Chinese, or Japanese, as we have seen in the introductory example of Chinesexiao and will see in some more detail below. This suggests that the features thatthe gackern and cackle lemmata express in contrast to lachen and laugh are notas central as those distinguished in the basic vocabulary of a larger number oflanguages, such as verbalized (lachen/laugh) vs. non-verbalized (lächeln/smile,grinsen/grin, etc.) LAUGH behavior. In other words, our main assumption is thatif more languages distinguish one feature of the LAUGH behavior than another


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feature, the more frequently distinguished behavior is more universally salient.Such an assumption of universality clearly goes beyond what Gipper (1959),Weisgerber (1951), or Hjelmslev (1969) would have subscribed to.

Hjelmslev (1969) emphasized the importance of comparing as many languagesas possible and analyzing the signs used to express the same thought (Saussure’ssignified) in order to identify the meaning of a given sign and delimit it from others.Since every language divides and structures the thought-plane differently, the samecontent-substance can be formed and shaped in a different way in every languageand reflected in different expression-substances. In Hjelmslev’s example, the con-tent-substance of the English word green is not identical to that of the Welsh wordglas. In addition to what English refers to as green, glas also covers part of thecontent-substance of the English words blue and gray, while another part of themeaning of green is covered by gwyrdd. It is clear from this example that languagesstructure the expression-plane – i.e., what is lexicalized in a language – differently.Based on this, languages can be assumed to also structure the content-plane – i.e.,what semantic differences are cognitively salient – differently.

In this context, two clarifications about central theoretical assumptionswe’re subscribing to should be made to conclude this section. First, while wedo not need to take a strong position in the debate around what is commonlycalled the Sapir-Whorf hypothesis, the lexical field approach in general, and ouradaptation of it in particular, presuppose that no word of English or any othernatural language is a semantic primitive. Rather, words are pointers to thecontent-planes, world models, or ontologies (cf. Nirenburg and Raskin 2004),of speaker and hearer. That is, in principle, we subscribe to Katz’s effabilityhypothesis (1976), which takes any expression of one language to be translata-ble into any other language, if not always with equivalent amounts and types oflinguistic material. Second, while using translations of a novel, our approach isnot primarily intended as a contribution to translation theory, in contrast toother lexical field studies like those by Wandruszka (1969,5 1979, 1984). We willmake some remarks in this direction, but reserve an exploration of that dimen-sion, especially where multiple translations are available, as in Turkish (Kesey2000b, 2002) and French (Kesey 1969, 2013), for a later study in our project. Thepresent study is an attempt to focus on semantic primitives, with the methodo-logical help of semantic features, in the lexico-semantic field at hand by findingthe basic lexicalized distinctions across a number of languages.

5 Wandruszka actually touches on the translation of lemmata in the LAUGH field (1969: 12–13),but without observations relevant to our study.


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3 Decontextualized lemmata: options and limits

3.1 The corpus

In line with the ‘translation method’ (Ogarkova, Soriano and Lehr 2012), inwhich the choices of the various translators are taken as an uncontested foun-dations, our study uses Kesey’s original English text as a basis for discoveringsemantic distinctions in the lexical field LAUGH by aligning the original uses inthe source language with their translations into several target languages tocreate evidence for the different mosaics of meaning.

Our work was furthermore inspired by Santana’s (2006) approach, if not inits main methodology. In the theoretical part of her study, Santana used lexicondefinitions to analyze meaning relations in the field of HUMOR, which raises theproblem of making the respective lexicographer the implied language expert,analogous to the translators remaining unchallenged as the language experts inour study. Obviously, a larger aligned corpus would have ameliorated this issue,but none was available that was translated into as many languages as thepopular work of fiction used here (e.g., aligned news corpora), was as recentin usage (e.g., works by Shakespeare; Kruger 2004), or could be expected tocover the target LAUGH field well (e.g., the JRC-ACQUIS Multilingual ParallelCorpus of European Union documents; Steinberger et al. 2006).

The corpus for this study has been built from the original text by Kesey (1962)and its translations into French (1969), German (1971), Italian (1976), Arabic (1981),Turkish (2000a), Japanese (2000b), Turkish again (2002), Spanish (2006), andChinese (2008), which were included in that order.6 Currently efforts are underway to add further Indo-European languages, including Polish Kesey (2003), whichhas already been fully aligned, and Russian and Estonian, which are in progress.

3.2 Aligning the corpus

The process of creating the multilingual corpus and aligning it required severalsteps. At the current stage of the project, our corpus comprises eight translations ofthe original English text, and includes all lexicalized instances of the LAUGH field

6 Our selection of target languages for the methodological development is partially opportu-nistic, i.e., was done on the basis of the availability of translations and collaborators for thelanguages that we couldn’t cover ourselves (see acknowledgements). The selection is becomingmore directed for the next stage of the project, taking into account the insights gained in thisfirst stage.


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and other, less frequent terms in paradigmatic relation to the core lemmata laugh orsmile (giggle, cackle, roar, etc.). This resulted in 396 tokens in the English text (seeTable 1 and its footnote) that were annotated with their relevant context, stemmed,and lemmatized in a spreadsheet (see Appendix A). Next they were matched withtheir translations in the target languages, for which the same information as forEnglish was noted and complemented with glosses and notes for the benefit ofresearchers who do not speak a given language (see Appendix B).7

An example of the tokens identified for the four most frequent lemmata inEnglish, and their translations in German and Turkish is presented in Table 1above. The second column lists the four most frequent English lemmata in orderof token frequency as given in the first column. The fourth column has the most

Table 1: English-German-Turkish core lemmata8.

ENG GER TUR

laugh lach- gül- prust- kahkaha / /

kıkır Neşe

grin grins- sırıt- lächel- gül-

/ kahkaha

smile lächel- gül- grins- sırıt- verzieh- / /

giggle kicher- gül- / kıkır kahkaha

7 Simultaneously, the reverse process was carried out: every translated text was searched forfurther tokens of the LAUGH field in that language for two purposes. On the one hand, it helpedto identify tokens that are in the English original and were overlooked previously. On the otherhand, tokens from the field in the translations were noted that did not have counterparts in theEnglish original.8 The remaining lemmata in English with their token counts are: snigger/snicker 5, chuckle 4,hoot 2, whoop 2, roar 2, snort 2, beam 1, bellow 1, bust up 1, holler 1, joke 1, pop 1, smirk 1, sneer1, squeak 1, tease 1, wink 1, funny 2, and several types of onomatopoeic tokens 8.


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frequently used translation lemma in German with the number of tokens incolumn 3. If the translator used a second, or further, lemma in German totranslate the English source lemma, these are listed in further column pairsalong with their token count. The right-most columns show the same data andcounts for Turkish.

Every instance of a token from the LAUGH field in the English text, includ-ing onomatopoeia (“Ho ho ho” Kesey 1962: 70, index number 108) and multi-word expressions (“whoops of laughter” Kesey 1962: 221, index number 300) wasmarked by the first author and checked against each of the translations by anative speaker of the respective languages, who in most cases was also alinguist. After the fifth round of checks against a new language, no more over-looked tokens for the English original had to be added to the corpus of 396tokens. For every item in the corpus, we proceeded as follows: the item was firststripped of inflectional morphology to obtain the base form; then derivationalmorphemes were stripped to arrive at the lemma form. For each lemma, themeaning was glossed and annotated by a native speaker researcher (cf. Table 2),and checked by a second native speaker, so that the material could becomeaccessible to linguists who don’t speak the target language.

The concept of lemma as used in this project requires further explanation. We useit to refer to each element of the core inventory of concepts, transcending wordclasses: e.g., “laughter” (n.; Kesey 1962: 221), “the laughing” (n.; Kesey 1962: 227),“they laughed” (v.; Kesey 1962: 229), “laughing gabble” (adj.; Kesey 1962: 142) areall counted as tokens of the lemma type laugh. Because of the hypotheses andgoals of our study, we wanted to distinguish between similar lemmata in order torepresent the core semantic distinctions in the field. Take as an example thelemmata ridere and sorridere in Italian, which are undoubtedly etymologicallyrelated (sorridere comes from Latin subridēre, with sub-, meaning “under” or“below,” and ridēre, meaning “laugh”). We nevertheless decided to considerthem as two separate lemmata under the assumption that despite the sharedetymology the two lemmata represent the core distinction between vocalized

Table 2: Chinese transliteration and gloss of to laugh at (index number 284).

Original: laughing at the girl

Transliterated Chinese translation: Chaoxiaochao-xiaobelittle.V-laugh.V‘to laugh at’


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(ridere, “laugh”) and non-vocalized (sorridere, “smile”) behavior. The same is truealso for non-Romance languages, such as German (lachen vs. lächeln) or Turkish,where the noun ‘smile’ is derived from ‘laugh’ by adding a diminutive suffix, ascan be seen in the example below (Table 3, index number 69). Thus, we rejectetymology as an unchallenged criterion for our semantic classifications.Nevertheless, grouping tokens into lemmata was even harder in other languages,especially Japanese and even more so Chinese, as briefly discussed below.

During the process of creating and annotating the corpus, we had to makefurther methodological decisions that were not always easy or obvious. First ofall, many LAUGH tokens in the corpus are made up of two or more lexical items.For this reason, we had to decide which element would be considered the targettoken. Given the purpose of the study, we decided to exclude tokens such asweak verbs (such as English do, be, have, and make), adjectives and adverbs,except when they are the only element in the phrase to carry the LAUGHmeaning component. For example, in the annotation of the Italian part of thecorpus, the expression scoppiò in una risata fenomenale (Kesey 1976: 122, indexnumber 134), which corresponds to the English original “he busted up laughingfit to kill,” (Kesey 1962: 86) was reduced to the lemma ris- as realized in the nounrisata. This ignores the syntactically parallel verb scoppiare, as it did not carryany of the LAUGH core concepts. The adjective fenomenale, which is obviouslyimportant, has been ignored as well in order to create a clean basis from whichthese additional phenomena could be analyzed later, taking contextual elementssuch as these into account. Based on the foundation presented here, futurestudies, as well as the next steps in our study, can then venture on an extendedapproach taking into account further context, such as adjectives before laughteror adverbs modifying laugh. These extensions can then consider the distinctionsmade in the basic vocabulary instead of ignoring or even overlooking them,without getting overwhelmed by a multifold increase of material and a lack oflinguistic focus on basic distinctions.

Table 3: Turkish translation and gloss of smile.

Original: her smile

Turkish translation: Gülücüğügül-ücüklaugh.V-NMLZ.DIM‘smile’


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In a comprehensive corpus study of German, for example, Huber (2011)found 270 different adjectives preceding the noun Lachen (“laughter”) alone.According to this study, these adjectives qualified laughter along the followingdimensions: emotional quality, mental state, motivational quality, social func-tion, degree of control, length, quality, physical state, regulation, intensity,acoustic quality, judgment, and other. Huber’s study struggled with these inter-secting and orthogonal classifications without even considering anchor termsother than Lachen (“laughter”) and the basic semantic contrasts marked bythese choices.

In Japanese, on the other hand, very little by way of a useful distinction ofmeaning can be indicated by the core lemmata alone. The majority of the 396tokens was translated into a form of wara- across the LAUGH field, including whatEnglish distinguishes as ‘laugh,’ ‘smile,’ ‘grin,’ etc. As illustrated in Table 4,Japanese needs to add adjectival or adverbial phrases to accompany wara- inorder to signal the relevant semantic differences lexicalized in English and inother languages structured similarly to English. As the examples in Table 4 show(index numbers 294, 314, 340), the meaning lexicalized in English as laugh isconveyed in Japanese by adding the adverbial phrase oogoede to warau; themeaning of giggle is conveyed by adding the onomatopoeic adverbial phrasekusukusu to the same lemma, and the meaning of grin is conveyed by addingthe ideophonic adverbial niyat (“laugh with a grinning face”).

According to the criteria we have chosen here, German, a language obviously veryclosely related to English, makes essentially the same distinctions as its cousin.The similarities between the LAUGH field in the two languages can be appreciatedin Figure 1, the first Circos visualization we introduce (see section 4), where thefour English core lemmata are mapped onto their corresponding German

Table 4: Examples for Japanese wara-.

English Japanese

laugh oo-goe- de wara-ubig.ADJ-voice.N manner.PRT LAUGH.NONPAST‘laugh with a big voice’

giggle kusukusu warai-kake-ruideophone LAUGH.throw.NONPAST‘throw [out] a suppressed laugh’

grin niyat- to wara-uideophone manner.PRT LAUGH.NONPAST‘to laugh with a grinning face’


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translations. The visualization has the following elements: (1) an outer circle withthe labeled segments that represent lemmata of English and of the target languageand are proportionate in length to the number of tokens per lemma, including thetoken count along the outside; (2) ribbons connecting the circle segments of anEnglish lemma and the target lemmata they were translated into. In Figure 1, wesee the four English core lemmata on the left side from top to bottom: giggle,smile, grin, and laugh. For example, laugh at the bottom left has the largestnumber of tokens in the corpus, as reflected by the width of its circle segmentalso marked with the count of 160. On the right side of the image are thecorresponding German translations of the English lemmata (lach, grins, laechel,

Figure 1: English-German correspondences of the core lemmata.


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kicher, etc.). Among those, we also included the item “none” to represent cases inwhich the English lemma was not translated into the target language. Thestructural similarity of the LAUGH field in both languages is apparent. Englishlaugh maps almost completely onto lach, as the light gray ribbon joining the twolemmata makes apparent by connecting the entirety of the lach circle segment tonearly the entirety of the laugh circle segment; similarly for grin and grins; smileand lächel; and giggle and kicher.9 However, in Chinese the field LAUGH is evenless distinguished than Japanese at the level of core lemmata. If we were togenerate a visualization of the field LAUGH in Chinese and English similar tothe one proposed in Figure 1, it would show all four English core lemmatamapped onto xiao, with only two exceptions: le (“happy”) and [zi-]chao (“ridicule”[oneself]).

4 Feature matrices: Sample results for humorresearch and contrastive linguistics

When analyzing the semantics of the lexical field of LAUGH cross-linguistically oneshould not forget that the lexical items are used to refer to emotional and cognitiveexpressions. Unlike the color spectrum, which belongs to the physical domain andcan be apportioned into colors arbitrarily (Berlin and Kay 1969; Kay et al. 2009),core emotions are assumed to be universally shared, (Ekman, Friesen and Hager2002). Therefore we expect that the terms of the LAUGH field do not segment themulti-dimensional sphere of underlying emotions and cognitive processes arbitra-rily, but rather along salient emotional (e.g., aggression, affiliation), acoustic (e.g.,volume, iteration), or visual (e.g., teeth exposed, eyes closed) lines.

For that reason we expected to find at hierarchy of semantic features that issocially and cognitively motivated and not arbitrary.10 The main assumption isthat it is important to be able to report different types of LAUGH, because theyhave very different social functions for emotional feedback, structuring conver-sations, and social relationships (Glenn 2003; Glenn and Holt 2013). The nextsection describes the methodology we used to generate theses matrices, which is

9 The few exceptions are represented by a narrow ribbon connecting laugh and none (nottranslated), another laugh token connected to prust, a single token-width ribbon going from grinto laechel, and three single token-width ribbons going from smile to verzieh, none and grins,respectively.10 This is of course not to say that the relation between the signifier and a signified is generallynon-arbitrary in a linguistic sign, on the contrary. But the relation of one signifier to anotherreflects the relation of their respective signifieds.


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based on classic approaches to semantic features (Bendix 1966; Lehrer 1969;Lipka 1979; Lyons 1977: 317–335), but has important refinements describedbelow. As such, this paper not only advocates a revival of semantic field theorybut also of binary semantic features (Greimas, 1987; Greimas and Courtés, 1979).

4.1 Hierarchical matrices of semantic features: Vocalizationand aggression

For each language we arranged the lemmata from most frequent to least fre-quent in terms of tokens.11 Next, we checked the two most frequent lemmata todecide what binary feature distinguishes them most strongly. In German, voca-lization distinguishes the most frequent lemma lach- (“laugh”) form the second-most frequent grins- (“grin”). This is a sufficient feature for their distinction andthe label is descriptively adequate as well. In general, these two factors forfeature selection were applied, in that order: distinctiveness and descriptiveness.The next lemma in order of frequency for German is lächel- (“smile”). It is silentlike grins, so the next binary feature to designate has to distinguish the two non-vocalized behaviors lächel- and grins-. While there can be non-aggressive grins,the dominant sense of the lemma carries an aggressive denotation. Lächel- byitself is not aggressive. Checking lach- for this new feature of aggression resultedin the judgment that it is neutral, because lach- is so general that there areaggressive and equally likely non-aggressive uses. Therefore lach- is markedwith an empty cell for the feature aggressive.12 The next lemma kicher- (“giggle”)is vocalized and as such already distinguished from silent lächel- and grins-. Thenext distinctive feature therefore has to contrast vocalized kicher- and lach-.Kicher- is characteristically high-pitched, whereas lach-, while unmarked formany features as the central lexeme of the field, is not high-pitched. The silentforms of LAUGH (kicher-/grins-), on the other hand, cannot be marked for pitch,because they are silent. Pitch presupposes vocalization; therefore the

11 Where we did not find a lexicalized core inventory, as in Japanese and Turkish, where wara-and gül-, respectively, dominate the field to a degree that no meaningful distinctions can bemade in the core vocabulary, we took into account (semi-)fixed expressions with adjectives,adverbs, and derivational morphemes marking the distinctions that the semantically eviscer-ated main lemmata cannot mark. As discussed, this was not possible in Chinese.12 This lemma could be either aggressive or non-aggressive, therefore the semantic feature of[aggression] is not distinctive for it, i.e., lach- cannot be contrasted to a lemma that is positivefor this feature and it cannot be contrasted to one that is negative. If two lemmata are identicalwith respect to the distribution of plusses and minuses and there is a difference only in emptycells to pluses, minuses, or other empty cells, then according to the matrix there is no differencebetween these two lemmata.


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presupposition to determine the polarity of this feature is not given for kicher-and grins-, which is marked with a slash, the other non-distinctive cell fillerapart from an empty cell.

The order of distinctive features, not further verified against other corpora, isfirst and foremost dependent on the token frequency of the lemmata in Cuckoo’sNest. The first feature [vocalization] is introduced to distinguish the most frequentlach- from the second-most frequent lemma lächel-. The second feature [aggres-sion] is introduced to distinguish the two silent lemmata lächel- and grins-. As aresult of this principle, the cell at the intersection of each new lemma row and thefeature column that is introduced for that lemma should not be empty (or have aslash for being non-presupposed). That cell should either have a+ or a– in con-trast to at least one other + or– in the column above that cell. If the matrix for agiven language does not follow the principle for a given lemma, a footnoteexplaining the exception is necessary. All subsequent lemmata and featureswere included in the matrices according to the same principles.

As hypothesized, the alignment and subsequent comparison of the coreLAUGH lemmata highlighted the existence of specific semantic features that thelexical field distinguishes across the languages under consideration, and severallanguage-specific features (see Table 5). Our hypothesis parallels that of the Berlinand Kay color study (1969), who found that if a language has two basic colorterms it will distinguish black and white, but if the basic color terms are three, itwill include red, and if the basic terms are four, either green or yellow will beadded, etc. Similarly, for all languages in our corpus and following our method, itseems that if a language has only two lemmata to cover the LAUGH field, it willdistinguish between a vocalized and a non-vocalized behavior (as in English smileand laugh). The notable exception is Japanese where tokens by frequency distin-guish most prominently self-directed (wara-) from other-directed behavior.

After distinguishing vocalized from non-vocalized LAUGH behavior, in mostlanguages analyzed so far, aggressive (It. -ghign-) and non-aggressive variantsare distinguished. These two core distinctions, vocalization and aggression, arenear-universals for our corpus. Further semantic features that could already be

Table 5: Semantic features that distinguish the most frequent lemmata by language(voc.= vocalized, self-dir.= self-directed, aggr.= aggressive, …-p.=…-pitched, o.=open).

ENG GER TUR ITA SPA FRE CHI JAP

st voc. voc. voc. voc. voc. voc. voc. self-dir.nd aggr. aggr. aggr. aggr. loud aggr. loud aggr.rd high-p. high-p. loud low-p. aggr. talking o. mouth intense


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identified as prominent include pitch and loudness differences among thevocalized LAUGH behaviors distinguished, e.g., English giggle [+ high-pitched]and Turkish kahkaha [+ loud]. Further distinctions with decreasing universalityand based on much weaker evidence given the current corpus include iterationor lack thereof of the vocalized behavior, involvement of movement of the bodybeyond the face, and exposure of teeth. These features will need to be confirmedby applying the established methodologies to larger corpora, as proposed.

4.2 Mismatches across semantic dimensions

The resulting matrix of language-specific semantic features is already a valuabletool that will be used to assess translation accuracy and analyze the role playedby context. Consider the following scenario: When a source lemma, character-ized by certain semantic features, is translated into a target lemma, character-ized by fewer or different semantic features, the result is a meaning mismatch.This mismatch could be solved only by opting for a different target lemma, ifavailable, or if the context supplied the missing feature. Otherwise, we couldconclude that a relevant part of the original meaning has been lost in thetranslation. An example of meaning mismatch in translation, extracted fromthe corpus, is presented below (Table 6).

The lemmata used in this example are English laugh, characterized by thesemantic feature values in the first row of Table 7, and Spanish carcajad-,characterized by the feature values in the second row (more on this below).

Table 6: English – Spanish meaning mismatch.

Original: this strikes me [Bromden] so funny I almost laugh

Spanish translation: suelto una carcajadacarcajad-aguffaw-SG.F‘to laugh out loud’

Table 7: Feature analysis of the meaning mismatch in the translation of laugh to Spanish.

[vocalized] [loud] [aggressive]

ENG laugh +SPA carcajad- + +


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The mismatch between the two lemmata occurs across the loudness dimension(see Table 7), for which laugh is neutral, but carcajad- has a positive value. As aresult, the Spanish translation of this specific token characterizes the vocalizedbehavior as a very loud one, as opposed to the English original, which is notmarked for loudness either way. However, the translation maintains the originalmarking for vocalized and unmarked aggressiveness.

In Arabic, the cognate lemma q-h-q-h appears only three times, and is usedto translate English giggle, thus maintaining the [+ vocalized] original Englishfeature of the lemma, but adding a [+ loud] feature, similarly to what happenedin the Spanish translation (Table 8). In this case, however, the translation ismoving further away from the original because the original [+ covert vocaliza-tion] of giggle is lost and substituted with the [+ loud] feature of q-h-q-h.Similarly, the Turkish lemma kahkaha, which shares its Arabic origins withthe Spanish carcajad-, is used 35 times to translate English laugh, once totranslate giggle, and once to translate grin. Therefore, kahkaha, besides addingthe [+ loud] feature to the translation of laugh, and supplementing the [+ covertvocalization] of giggle with [+ loud], also moves one step further and substi-tutes the [-vocalized] feature of grin with the features [+ vocalized] and [+ loud],radically modifying the meaning conveyed in the translation. Given that thetranslated context in Arabic has no semantic cues to counterbalance the[+ loud] feature either, our hypotheses are that we either have a mistranslationor a forced variation in Arabic, where 3 tokens of giggle were translated usingthe lemma q-h-q-h- in a context with a large number of LAUGH tokens.

For comparison, the same token of grin that was translated as kahkaha inTurkish, resulting in a mismatch across the vocalized dimension, was translatedas sonrisa in Spanish, thus using a lemma that shares the same semanticfeatures as the original grin, as shown in Table 9.

Table 8: Feature analysis of the meaning mismatch in thetranslation of giggle to Arabic and Turkish.

[vocalized] [covert voc.] [loud]

ENG giggle + + –ARA q-h-q-h + – +TUR kahkaha + – +


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Over the last decade, information visualization and visual analytics have beensuccessfully employed as heuristic tools in many fields (Keim et al. 2010;Thomas and Cook 2005). In our study, additional results emerged from thevisualization of the data with Circos (Krzywinski et al. 2009) as well. Some ofthese will be discussed here in order to discuss this final methodology broughtto bear on our corpus. We already saw this method in Figure 1 that illustratedthe high degree of structural (and etymological) similarity between English andGerman.

Figure 2 shows how the translations of the Spanish tokens correspond to thetokens of the four main lemmata in English. The organization of the figure isidentical to Figure 1. The ribbons between the lemmata clearly show the impor-tance of the distinction between vocalized laugh and giggle corresponding to re-and non-vocalized grin, respectively, and smile corresponding to sonre-. Theonly other prominent correlation, between laugh and carcajad-, illustrates thelatter as a vocalized behavior as a translation of laugh, with the differenceaccounting for higher-intensity vocalized behavior as the contexts of thosetokens that were translated into carcajad- illustrate (also see above). Note thatthis is one of the only instances of foreshadowing of how our method can be thebasis of a complementary look at further distinctions expressed in the context ofthe core vocabulary of the LAUGH field. Analyzing the 26 tokens of laughcorresponding to carcajad-13 yielded the following additional lexical items inthe English contexts (with instance numbers) that indicate LAUGH behavior ofhigher intensity: let loose and laugh (22), leans back to laugh (31), too tight for

Table 9: Meaning mismatch between English (grin), Spanish (sonris-), and Turkish (kahkaha).

Original: … the wild grin …

Spanish translation: terrible sonrisasonris-agrin-SG.F

‘terrible grin’

Turkish translation: kahkaha üst-ü-n-e kahkaha taze-le-meklaughter.N top.N-POSS.SG-BUFF-DAT laughter.N fresh.ADJ-VBLZ-INF toput laughter on top of laughter

13 Ten out of the 26 tokens are used to describe McMurphy; none of these are used to describeRatched.


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laughing (72), a real laugh (100), he’s laughing so hard (126), etc. Conversely onecan check the context of those instances of re- that were used to translate gigglefor contextual cues that tell Spanish readers that this vocalized behavior is ofhigher pitch, lower volume, or covert nature than other instances of re-: reír ahurtadillas (index number 54, “laugh in a sneaky way”), se ríe entre dientes(index number 123, “laugh between the teeth”), soltar una risita (index numbers119, 177, 251, 314, 350, “to release a little laugh”).

The visualization for Turkish (Figure 3) shows that this language is structu-rally and etymologically more different from English than Spanish and Germanare. While the latter three belong to the same macro-family of Indo-European

Figure 2: English-Spanish correspondences of the core lemmata.


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languages, Turkish is a Turkic language whose vocabulary shows overlap onlyaccidentally with Indo-European or other languages through borrowing. Onerelevant item here has already been mentioned, namely kahkaha, which isborrowed from the same Arabic root as Spanish carcajad-. Dominant for theTurkish LAUGH field is the lemma gül-. It served to translate mostly the voca-lized English lemmata (laugh), while the obviously related gülüm- translatedanother non-vocalized behavior, smile, similarly to what happens in Spanish(re- and laugh, sonre- and smile; cf. also lach- and lächel-). However, laugh alsofed prominently into kahkaha-, a situation very similar to Spanish, reflecting thesame [+ loud] feature of both kahkaha- and carcajad- as discussed above.

Figure 3: English-Turkish correspondences of the core lemmata.


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Of further note is the fact that sırıt- is fed almost exclusively by grin,reflecting the shared general tendency to describing aggressive behavior ofthese two lemmata, possibly with stronger aggression in sırıt-, with otherforms of laughter going to gülüm-. This is supported by the fact that almost notoken of smile is translated into sırıt-.

The main distinction between vocalized and non-vocalized behavior isestablished in the basic LAUGH vocabulary of Turkish as well with gül-, kah-kaha-, and kıkır- as [+ vocalized] while gülüm- and sırıt- are [− vocalized].Nevertheless, the nontrivial number of non-vocalized grin and smile tokensthat were translated to gül- confirm the unity of the field as a paradigmaticchain, in other words, the continuity from laughing to smiling.

Similar to the significant non-alignment between grin and sırıt-, pointing toa semantic feature that grin does not distinguish, some of the giggle tokens beingtranslated as kıkır- indicates that Turkish makes a further distinction, possiblyrepresenting more iconic onomatopoeia in kıkır- than in giggle, which isobscured in the basic vocabulary item in English.

5 Conclusions and outlook

Before we conclude, let us take a look at the distribution of tokens in relation tothe characters and the plot of the novel (see also Tanner 1973). Two clear peaks ofLAUGH behavior appear in the novel: McMurphy has a dominant passage whenhis character is introduced on page 16 and all the way to page 29, while Ratched’ssmile “which she can turn into whatever expression she wants” (Kesey 1962: 47)is introduced on page 43. McMurphy’s laugh dominates (74 tokens out of the totalof 155 laughs); he grins frequently (35), rarely smiles (4), and giggles twice. NurseRatched predominantly smiles (46 out of the total of 80 smiles), grins once, andnever laughs or giggles. Clearly, McMurphy’s laugh, often loud and shared, con-trasts with Ratched’s silent, controlled and controlling smile. Even without takingthe context of the tokens into account, the translations tell us that McMurphy’slaugh is [+ loud], as testified by its translation into lemmata that carry that featurewhen they are available in a target language, e.g., Spanish carcajad (11 tokens).Moreover, the variety of McMurphy’s reactions supports the assumption of theirgenuineness, while Ratched uniformly displays a domineering fake smile,expertly enacted without the FACS action unit 6, crinkling of the eyes, theindicator of a genuine enjoyment smile (Ekman et al. 2002), by Louise Fletcherin Miloš Forman’s 1975 eponymous movie.

Besides characterizing the two main protagonists of the novel, LAUGHbehavior marks key turns in the novel’s plot: When the narrator, Bromden,


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first sees McMurphy enter the laughterless ward, his description includes nofewer than 10 laughs (all of them translated as carcajad- in Spanish), 2 grins, and1 smile on a single page (Kesey 1962: 16). McMurphy laments the lack of theinmates LAUGH behavior, using 7 tokens of laugh, as an expression of their lostfreedom, in particular masculinity (Maxwell 1970) in his first group session(Kesey 1962: 65f). In the overall distribution of LAUGH events, there are twoconspicuous gaps, the first around page 115, corresponding to Bromden’s non-humorous hallucinations, and the second around page 150, where McMurphyhas the devastating realization that the other patients have voluntarily com-mitted themselves. The first sound McMurphy hears Bromden make is a chucklethat turns into a laugh just before Bromden addresses his first words toMcMurphy before they laugh together (Kesey 1962: 184f). McMurphy succeedsin bringing the other patients on the boat trip where they rediscover their ownlaughter and he himself for the last time “spreads out his laughter” (Kesey 1962:211) and also succeeds in bringing laughter into the ward itself during theovernight party. There’s a final LAUGH trichord with the inmates grinning andsmiling at Ratched with “… McMurphy’s presence still tromping up and down thehalls and laughing out loud… “ (Kesey 1962: 263f).

Using our new literature-based corpus and methodological refinements –centrally of lexical field theory, but also by reviving semantic features andintroducing visual analytics methods into semantics – we have documentedthe value of these refinements for the study of paradigmatically delimited lexicalfields and illustrated this with a substantial number of sample analyses. The keyfeatures of vocalization and aggression have been found to be near-universals inour corpus with respect to LAUGH. We have argued for context-insensitivelexical field theory as the basis for context-sensitive methods, rather than deny-ing core meanings of lexical items and finding meaning in the context alone.Both the methods and their detailed documentation are submitted as hopefullyvaluable tools for semanticists and literary scholars.

Obviously, these types of analyses can be carried out in much more detailfor the other languages already curated into our corpus and those that are beingadded. Future reports on the project can now refer back to the foundationaldiscussion of the theories, methods, and concepts in the present paper and focuson issues across all languages, language pairs with or without pivoting on theEnglish original, or language families of relevance for humor researchers, com-parative linguists, and translators.

Acknowledgements: We would like to thank the following people for collectingand annotating the lexical material in different translations of Ken Kesey’snovel, not all of which could be considered for the present paper, over the last


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nine years: Adel Aldawsari (Arabic), Władislaw Chlopicki (Polish), Hilal Ergül(Turkish), Meichan Huang (Chinese), Liisi Laineste (Estonian), Shigehito Menjo(Japanese). The other languages were analyzed by the authors themselves. Wewould also like to thank those who double-checked the analyses of the indivi-dual languages, did earlier versions of the alignments, or provided technologicalsupport: Oğuz Akgüngör (Turkish), Hessah Aldayel (Arabic), Ursula Beermann(German), Alberto Miras Fernández (Spanish), Todd Morris, Brett Pierce(English), Andrea Samson (French), Ünal Zakoğlu (Turkish), and Ying Zhang(Chinese). Finally, we’d like to thank the two anonymous reviewers for theirhelpful comments.

References

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COPY AUTHOR'S · 2018. 8. 5. · AUTHOR'S COPY Christian F. Hempelmann* and Elisa Gironzetti An interlingual study of the lexico-semantic field LAUGH in Ken Kesey’s One Flew over