“Geiriau Saesneg yn slipio i fewn”: Investigating the integration of English-origin verbs in Welsh

Jonathan Stammers8 March 2010, Bilingualism Centre

Overview The Siarad corpus Code-switching vs. borrowing controversy Poplack approach: “Nonce Borrowing” English-origin verbs in Welsh Analysis: Soft mutation on verbs (2 attempts) Dealing with word frequency effects Summary

The Siarad Corpus 40 hours of Welsh/English bilingual speech recorded

& fully transcribed in CHAT format 69 Naturalistic recordings of informal conversations,

typically between 2 speakers, & 30 minutes long; 151 speakers of varying age, sex and background 456,266 words (tokens) Every word tagged for language Recordings & transcription done by project team

(Elen Robert, Peredur Davies, Marika Fusser & myself; Margaret Deuchar – project director)

Freely available to researchers online

Examples in Siarad: Borrowings?

ond mae o mor cheesy mae’n funny yndy ?

“but it’s so cheesy it’s funny isn’t it?” [Fusser29:217]

hynna ’dy’r exam dw i gorod eistedd fory

“that’s the exam I have to sit tomorrow.” [Stammers6: 1273]

Code-switching or Borrowing? Criteria:Criterion Borrowing Code-mixing

no more than one word + -

adaptation: phonological ±/+ ±/-

morphological + -

syntactic + -

frequent use + -

replaces own word + -

recognised as own word + -

semantic change + -(Muysken 2000: 73)

Additional Criteria suggested:“Core/Cultural” distinction: “Cultural” items are not switchesFlagging: self-correction, repetition, hesitation or stammering flags up a switchDictionary

Poplack’s approach Code-switching and borrowing can be

distinguished absolutely “Free morpheme constraint” no word-

internal switching Variationist approach: Comparing morpho-

syntactic patterning of donor-language items with native items

“Nonce Borrowing hypothesis”

The Nonce borrowing hypothesis “One of the goals of these studies is to develop operational criteria

for distinguishing loanwords from codeswitches. Thus, for the Puerto Rican data, a working hypothesis was that loanwords from English were phonologicaly, morphologically, and syntactically integrated into Spanish, were recurrent and widespread, and that an English word not satisfying these criteria could only occur in English monolingual discourse or in code-switches from Spanish to English. In general, however, borrowing is a much more productive process and is not bound by all of these constraints. In particular, phonological integration and the “social” characteristics of recurrence (in the speech of an individual) and distribution (across the community) need not be satisfied. This type of borrowing is sometimes called “nonce” borrowing.”

(Sankoff, Poplack & Vanniarajan 1990: 74)

Study Language pair studied

Elements analysed

Linguistic features studied in analyses

Conclusion

Sankoff, Poplack & Vanniarajan 1990

Tamil-English Lone English nouns

Case inflections All are borrowings

Poplack & Meechan 1995

Wolof-French; Fongbe-French

Lone French nouns

Definite/indefinite reference ; NP word order

All are borrowings

Adalar & Tagliamonte 1998

Turkish-English Lone English nouns

Vowel harmony; Plural affixation; NP word order

All are borrowings

Budzhak-Jones 1998

Ukrainian-English Lone English nouns

Case inflections All are borrowings

Eze 1998 Igbo-English Lone English verbs; Lone English nouns

Affix distribution; serial constructions; vowel harmony (verbs) ; determiners; type of nominal reference; NP word order (nouns)

All are borrowings

Samar & Meechan 1998

Persian-English Lone English nouns

Definite/indefinite reference; VP word order; case inflections

All are borrowings

Turpin 1998 (Acadian) French-English

Lone English nouns

Determiners; NP word order; plural marking; discourse flagging

Most are borrowings; Minority are switches

Arroyo & Tricker 2000

Catalan-Spanish Lone Spanish nouns

Definite/indefinite reference; plural marking; gender

All are borrowings

Shin 2002 Korean-English Lone English nouns

Case inflections All are borrowings

Cacoullos & Aaron 2003

Spanish-English Lone English nouns

Determiners All are borrowings

English verb insertions (1)

More “established English borrowings”:

pasio (to pass), trio (to try), setlo (to settle), canslo (to cancel), meindio (to mind), cysidro (to consider)

sut mae o’n cope-io efo (.) hynna i gyd?

“how is he coping with all that?” [Fusser29:635]

pan dach chi’n defnyddio wide-angle lenses dach chi’n emphasize-io ’r foreground.

“when you use wide-angle lenses, you emphasize the foreground.” [Fusser17: 792]

English verb insertions (2)

bysai hi’m ’di gwisgo helmet ’sai pen hi ’di cael ei crush-o to bits

“if she hadn’t worn a helmet, her head would have been crushed to bits.” [Robert3: 898]

a mae ’di cael ei ºgonnect-io i’r printer yr computer, de

“and it’s been connected to the computer printer, right.” [Roberts2: 627]

English verb insertions (3)

anyway, ges i ’yn gazump-io ar hwnna “anyway, I got gazumped on that one” [Fusser29:700]

maen nhw’n (.) exfoliate-io chdi gynta (.) ac yn spwnjo chi drosodd gynta

“they exfoliate you first, and sponge you over first” [Fusser30:27]

Soft Mutation in Welsh

Soft mutation on verbs: Environments (1)

After "i" particle

e.g. oedd e’n mynd i ºgostio pres [Fusser6:524]

After "ei" possessive (with masculine subject)

e.g. fyswn i licio ei ºfenthyg o [Fusser9:375]

After various other particles: heb, am, cyn, gan, ar, neu; dy possessive

e.g. sut mae o am ºfihafio [Fusser15:510]

Soft mutation on verbs: Environments (2)

With gwneud (or ddaru) auxiliary + Subject

e.g. wnest ti ºdrio? [Stammers5:708]

After "i" + (non-overt) Subject

e.g. mae’n gwneud i chdi ºgofio rywbeth dydy? [Stammers7:139]

After Finite Verb + Subject

e.g. sut fedra i ºddeud? [Fusser4:257]

Soft Mutation: VariationE.g. Welsh verb “cerdded” (to walk):

a maen nhw’n mynd i ºgerdded am tua dwy, dair milltir“and they’re going to walk for 2 or 3 miles” [Roberts2: 32]

But frequently mutation doesn’t happen where expected (especially in informal spoken Welsh):

a (.) does dim byd i poeni amdano“and there’s nothing to worry about” [Fusser14: 40]

Three groups of verbs compared in this study (1st Analysis):

Native Welsh: cofio (remember), defnyddio (use), cwyno (complain), pwyso (push), cneifio (shear), treiglo (mutate), twtio (tidy)

talu (pay), penderfynu (decide), poeni (worry), lladd (kill), cwrdd (meet), cau (close), dal (hold), dechrau (start), cael (have), mynd (go), gweld (see). [irregular verbs and non –(i)o suffix included]

Listed English: trio, cario, clirio, dreifio, sbwnjo, clariffeio, pinsio, bargeinio, pipo, dipio, trotio, manejio, tsiecio, titso, protestio, cidnapio

twtsiad, dripian, [non –(i)o suffix included] Unlisted English: text-io, download-io, brief-io, quote-io, bulk-

io, ban-io, bypass-io, crush-o, trample-o, base-io, connect-io, babysit-io, decorate-io, concentrate-io, mollycoddle-io, power-walk-io

Method

Text-based searches through corpus (and using word frequency lists) for possible verbs, extracting examples where mutation expected (and where consonant can be mutated!)

Coded each verb as mutated or not First attempt: used a random sampling

technique to find the native Welsh verbs

Results (First Attempt): (1)

Results (First Attempt): (1)

Absolute Freq.

Freq./million words

%Mut. Overall AVG Freq

log(AVG freq)

1-4 1-9 34.69% 2.21 0.3452

5-45 10-99 52.68% 16.44 1.2160

46-450 100-999 75.29% 161.56 2.2083

451-4500 1000-9999 89.63% 1962.67 3.2928

Correlation coefficient with overall % mutation: 0.7752 0.9936

1-9 10-99 100-999 1000-99990

102030405060708090

100

Frequency per million words of verb (grouped data)

% M

uta

tion

wh

ere

exp

ect

ed

Analysis: 1st & 2nd AttemptsEarlier Analysis Later Analysis

Corpus 46 transcript subset (66%) of Siarad corpus; 301,072 word tokens

Whole Siarad corpus

(69 transcripts; 456,266 word tokens)

Instances selected (where soft mutation expected)

All English-origin verbs (with any suffix or none); Sample of native tokens: 5 randomly distributed tokens per transcript of any Welsh verbs, including irregular verbs 466 tokens altogether (230 native Welsh; 198 listed English; 38 unlisted English)

All English-origin and native tokens ending in the –(i)o suffix (regular verbs only)

506 tokens altogether (143 native Welsh; 302 listed English; 61 unlisted English)

No. of verb types (overall and by verb status and frequency band)

147 types overall

native Welsh: 65; listed English: 62; unlisted English: 20

1-9 words per million: 42; 10-99: 54; 100-999: 41; 1000-9999: 10

159 types overall

native Welsh: 44; listed English: 81; unlisted English: 34

1-9 words per million: 79; 10-99: 72; 100-999: 7; 1000-9999: 1

Initial consonants

Verbs starting with /p/,/t/,/k/,/b/,/d/,/m/, /ɬ/ and /g/ included; /rʰ/ excluded

Verbs starting with /p/,/t/,/k/,/b/,/d/ and /m/ included;

/ɬ/, /g/ and /rʰ/ excluded

Other Possible Variables: (1) Mutation Environment

(A) "i" particle

(B) "gwneud" auxiliary + Subject

(C) "i" + (non-overt) Subject

(D) "ei" possessive

(E) Fin Verb + Subject

(F) other particle

A B C D E F0

10

20

30

40

50

60

70 65.460.5

65.3

58.1

44.8

62.5

Mutation Environment

% M

uta

tion

wh

ere

exp

ecte

d

Other Possible Variables: (2) Initial Consonant

b d m p t k0

10

20

30

40

50

60

70

80

47.5

59.3 61.555.8

6468.8

Initial Consonant of verb

% M

uta

tion

wh

ere

exp

ecte

d

Three groups of verbs compared in this study (2nd Analysis):

Native Welsh: cofio (remember), defnyddio (use), cwyno (complain), pwyso (push), cneifio (shear), treiglo (mutate), twtio (tidy)

Listed English: trio, cario, clirio, dreifio, sbwnjo, clariffeio, pinsio, bargeinio, pipo, dipio, trotio, manejio, tsiecio, titso, protestio, cidnapio

Unlisted English: text-io, download-io, brief-io, quote-io, bulk-io, ban-io, bypass-io, crush-o, trample-o, base-io, connect-io, babysit-io, decorate-io, concentrate-io, mollycoddle-io, power-walk-io

1-9 10-99 100-999 1000-99990

10

20

30

40

50

60

70

80

90

100

Word frequency per million words (grouped values)

% M

utat

ion

whe

re e

xpec

ted

Results: Second Analysis

Native Listed Eng. Unlisted Eng.0

50

100

150

200

250

73%

66%

16%

27%

34%

84%

MutatedNot Mutated

Results: First & Second Analyses

Results: 1st & 2nd Attempts

Earlier Analysis Later Analysis

% Mutation by verb status

native Welsh 85.6%; listed English 61.1%; unlisted English 18.4%

native Welsh 72.7%; listed English 66.2%; unlisted English 16.4%

% Mutation by frequency band

1-9 words per million 34.7%;

10-99 52.7%;

100-999 75.3%;

1000-9999 89.6%

1-9 words per million 40.9%;

10-99 58.9%;

100-999 74.9%;

1000-9999 86.7%

Results (First Analysis)

Results (Second Analysis)

Statistical Testing: 1st & 2nd AnalysesEarlier Analysis Later Analysis

Results of statistical testing (logistic regression) with raw frequency values

Raw frequency marginally significant (p=.044) or not quite significant (p=.072) as a predictor of mutation, depending upon baseline category

Differences between all verb categories significant, including between native Welsh and listed English (where p<.0005)

Raw frequency marginally significant (p=.042) or not at all significant (p=.682) as a predictor of mutation, depending upon baseline category

Differences between verb categories significant, except between native Welsh and listed English (where p=.174)

Results of statistical testing (logistic regression) with log values of frequency

Log frequency significant (p=.019 or .01 with native and listed English as baseline respectively) as a predictor of mutation

Differences between native Welsh and unlisted English, and between listed and unlisted English significant (p=.019 and .03, respectively), but difference between native Welsh and listed English not at all significant (p=.448)

Log frequency highly significant as a predictor of mutation (p=.001) with listed English as baseline, but not at all significant (p=.549) with unlisted English as baseline)

Differences between native Welsh and unlisted English, and between listed and unlisted English highly significant (p=.001 and .005, respectively), but difference between native Welsh and listed English not at all significant (p=.186)

Summary English-origin verbs in Welsh – highly productive

(―(i)o) suffix). Almost certainly be considered a simple case of borrowings according to Poplack

Subset of them based on a dictionary criterion found to be significantly less integrated morpho-syntactically (with respect to soft mutation) : could be considered “switches”

Strong (log-linear) relationship between word frequency and rate of mutation

This goes against Poplack’s “nonce borrowing” hypothesis: “nonce” items pattern significantly differently from “established” items, based on either dictionary criterion OR frequency