Linguistic Structure in AggregateVariation

John NerbonneRijksuniversiteit Groningen

Summer, 2005

Aggregation in Variation

Thesis: Language variation must be studied in the aggregate.

• Detailed studies of single features ([aI] vs. [a], [æ] vs. [æ@])are at best inconclusive, at worst misleading.

• Bloomfield (1933) noted how confusing details are; Coseriu(11956,1975) warned against “atomism” in dialectology.

• But question: is the aggregate linguistically structured?

We focus here on the question of linguistic structure.

1

Outline

• Question

• Aggregating Technique

• Experiment on Southern Vowels in LAMSAS

• Results

• Reflections

2

Question

Aggregate pronunciation distance:

• Is reliable, given > 20 pronunciations/site (Cronbach α > 0.8)

• Correlates with naive speakers’ judgements (r ≈ 0.65)Gooskens & Heeringa (2003), Heeringa (2004: Chap. 7)

• Is predictable from geography (Heeringa & Nerbonne, 2001)

• Provides analytic foundation for dialect continua as organizingprinciple

But there’s little assumption of linguistic structure in this work.

Question: What linguistic elements determine aggregatepronunciation distance (if any)?

3

Factor Analysis

• Extract from correlation matrix those elements which reliablycorrelate

• Used in social science research to find common (underlying),e.g., in questionnaires

– Check reactions to local dialect vs. standard– Status factor: intelligence, education, knowledgeable– Sympathy factor: honest, sympathetic, unpretentious

• Leading idea: examine correlations among linguisticvariables, extract commonalities

4

Material

• Separate LAMSAS material into roughly 200 vowelpronunciations

– first vowel in <Alabama>, last vowel in <good morning>

• For each vowel, for each pair of sites, measure distance invowel pronunciation

– use LAMSAS feature chart as basis for distance

• Given that factor analysis will identify vowel occurences thatfunction similarly (in distinguishing sites), the linguistichypothesis is that these will reflect linguistic structure(phonemic identities, phonological processes).

5

Sites Grouped to Complete Matrices

999

1717

171717

1729

292929

17171744

4

44

44426

264

13

2626

26

1313

131313

13131313 4

6136

666 29

292923

232323 1616

242424

24

16

2424

2424

3 333

16161616

16

279927

2727

2714

1427

99

17272727

9

99

32

321414

14

25

14

5514

525 5

5555

1111

3 33

52525252525

2511

11 11 111121

3232

1919 1915 19

15 19 19 19

9

329

328

3232

328

8 32

15 1515

1526 15

2

26

10

26

18 1818

21212111 21

2121

3021 21

2130

1212

3030

77

121212

77

7

77

77

7

1119

3030

227

7

19

22

19

22222

2

22

222

2020

2

2020

2020

20

313131 31

10

1010

1010

10

28

28

28

2828

281

11

6

Site Matrices

Per vowel we obtain a distance matrix (site × site):

Wheeling Winston Raleigh Richmond CharlotteWheeling 0 41 44 45 46Winston 41 0 16 34 36Raleigh 44 16 0 37 38Richmond 45 34 37 0 20Charlotte 46 36 38 20 0

We then derive for each pair of vowels, the correlationcoefficient, i.e., the degree to which they indicate the samedistance between sites.

7

Vowel Matrix

Per vowel-pair we obtain correlation coefficient (vowel × vowel)correlations:

morning1 Tuesday2 pallet2 thunderstorm2 first1morning1 1 0.02 −0.01 0.73 0.056

Tuesday2 1 0.23 −0.03 0.02

pallet2 1 0.006 0.09

thunderstorm2 1 0.043

first1 1

This CORRELATION MATRIX is analysed for COMMON FACTORS.

We used varimax as an estimation procedure (in R): onlyorthogonal, no oblique rotations.

Condition: KCM/Bartlett’s test of sphericity (variables aresufficiently distinct): p < 0.001

8

Loadings

1 3 5 7 9 11 13 15 17 19

Factor Analysis

Factor

SS

load

ings

010

2030

9

Importance of Factors

1 3 5 7 9 11 13 15 17 19

Factor Analysis

Factor

Pro

port

ion

Var

0.00

0.05

0.10

0.15

10

Extreme Factor Loadings

1

2 3Arkansas 1Georgia 1New York 2forty 1wardrobe 1good morning 2thunderstorm 3Saturday 2

weatherboarding 2weatherboarding 3

Virginia 1hundred 2

thunderstorm 2

year ago 2

yesterday 2

Sunday before last 4

froze over 3

twice as good 2

Maryland 1

January 3

South Carolina 2

St. Louis 2

Cincinnati 2

February 3

Baltimore 2

Tennessee 2Tuesday 1Florida 1

Missouri 2closet 2

Massachusetts 4

what tim

e is it 4hundred 1kitchen 2pallet 2

white ashes 3N

ew Y

ork 1C

hicago 1draining 2

Sunday before last 2

Sunday week 2

foggy 2Thursday 2

Wednesday 2

Tuesday 2

Saturday 3

forty 2

thirty 2

Georgia 2

yest

erda

y 2

Ark

ansa

s 1

hund

red

2

Geo

rgia

1

thun

ders

torm

3

weath

erbo

ardi

ng 2

Satur

day

2

good

mor

ning

2

New Y

ork 2

Virgini

a 1forty 1

thunderstorm

2

wardrobe 1

year ago 2

weatherboarding 3

Sunday before last 4

New

Eng

land

1N

ew Y

ork

1T

uesd

ay 1

Flo

rida

1S

t. Lo

uis

2hu

ndre

d 1

Ten

ness

ee 2

Cin

cinn

ati 2

Bal

timor

e 2

Mas

sach

uset

ts 4

clos

et 2

kitc

hen

2M

isso

uri 2

palle

t 2C

hica

go 1

Sout

h Car

olin

a 2

white

ash

es 3

what t

ime

is it 4

Febru

ary 3

drain

ing 2

January

3

good morning 1

good day 1

Sunday before last 2Sunday week 2Tuesday 2Wednesday 2Thursday 2Saturday 3thirty 2forty 2Georgia 2

foggy 2

11

Extremes on Factors 3 & 2

0.0 0.2 0.4 0.6 0.8

0.0

0.2

0.4

0.6

Factor 3

Fac

tor

2

Alabama4

Cincinnati2February2

February3

Georgia2

January3Massachusetts1

Missouri1

Russia2

South_Carolina2

Tennessee3

Washington1

a_little_ways3

backlog2

bottom1

canal2

chair1

chimney2

clearing_up1

driven1

dry_spell1

dry_spell2

eight1

first1

frost1

good_morning3

half_past_seven3

hearth1

night1

one1

rising2

rose1

seventy1

sixth1

steady_drizzle2 steady_drizzle3

sundown2

thirty1

thunderstorm1

twenty−seven2

twenty1

twenty2

weatherboarding4

white_ashes1

yesterday3

12

Extremes on Factors 3 & 1

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

Factor 3

Fac

tor

1

April2

Arkansas2

Baltimore1

Baltimore2 Chicago1Cincinnati2

February2

February3

Florida2

Florida3

January3

Kentucky2

Kentucky3

Massachusetts1

Missouri1

North_Carolina2

Russia1

Russia2

Sunday_before_last1

Sunday_before_last2

Sunday_week1

Sunday_week2

Tennessee3

Washington2

Wednesday2

bottom1

chair1

chimney2

closet2

draining2

dry_spell1

eight1

first1

five1

frost1

good_day1

good_morning1

good_morning3 gully1

gully2

he_died_with2

hearth1

hog_pen1

kitchen2

my_wife1

pallet2

rising2

roof1seventy2

soot1

steady_drizzle1

steady_drizzle3

sundown1

sunup1

sunup2

thirty1

thunderstorm1twenty1 twenty2

twice_as_good3

umbrella1

umbrella3

what_time_is_it4

white_ashes1

white_ashes3

13

Extremes on Factors 3 & 2

0.0 0.2 0.4 0.6

0.0

0.2

0.4

0.6

0.8

Factor 2

Fac

tor

3

Alabama4

Cincinnati2

February2

February3

Georgia2

January3

Massachusetts1

Missouri1

Russia2

Tennessee3

Washington1a_little_ways3

backlog2bottom1

canal2

chair1

chimney2

driven1dry_spell1

dry_spell2

eight1first1

frost1

half_past_seven3

hearth1

night1

one1

rising2

rose1

seventy1

sixth1

steady_drizzle2

steady_drizzle3

sundown2

thirty1

thunderstorm1

twenty1

twenty2

weatherboarding4

white_ashes1

yesterday3

14

Extremes on Factors 3 & 2

0.0 0.2 0.4 0.6

0.0

0.2

0.4

0.6

0.8

Factor 2

Fac

tor

3

Alabama4

Cincinnati2

February2

February3

Georgia2

January3

Massachusetts1

Missouri1

Russia2

Tennessee3

Washington1a_little_ways3

backlog2bottom1

canal2

chair1

chimney2

driven1dry_spell1

dry_spell2

eight1first1

frost1

half_past_seven3

hearth1

night1

one1

rising2

rose1

seventy1

sixth1

steady_drizzle2

steady_drizzle3

sundown2

thirty1

thunderstorm1

twenty1

twenty2

weatherboarding4

white_ashes1

yesterday3

15

Factor 1 Loadings

closet2 0.884 kitchen2 0.880pallet2 0.874 white ashes3 0.869Tennessee2 0.856 Cincinnati2 0.851Baltimore2 0.844 Massachusetts4 0.830Chicago1 0.816 draining2 0.812

[@] vs. [1]

Florida1 0.842 [O] vs. [A] St. Louis2 0.821 [u] vs. [0]hog pen1 0.585 [O] vs. [A] Tuesday1 0.796 [u] vs. [0]

Missouri2 0.857 [0@] vs. [0@ffl]

16

Factor 1: Geography

17

Phonological Alternations Factor 1

[@] vs [1] [O] vs [A] [u] vs. [0]

Conclusion

• The first factor is sensitive to phonological alternations alongthe North-South division

18

Factor 2 Loadings

weatherboarding2 0.936 Saturday2 0.926Virginia1 0.905

[Vr] vs. V] (including [Ä] vs. [@])

good morning2 0.929 New York2 0.922forty1 0.906 thunderstorm3 0.893

[O@] vs. [Oˇ @]

19

Factor 2: Geography

20

Phonological Alternations Factor 2

[Ä] vs. [@] [O@] vs. [Oˇ @]

• The second factor is sensitive to alternations distinguishingthe Piedmont area, especially the absence of syllable final [r].

• Does [r]-lessness promote the lowering of [O]?

21

Factor 3 Loadings

Wednesday2 0.967 Saturday3 0.961thirty2 0.928 foggy2 0.854

[1ˆ ] vs. [1]

Georgia2 0.876 Tennessee1 0.766sofa2 0.760 good day1 0.775Russia2 0.751 good morning1 0.738

[@] vs. [1] (!)[E] vs. [Eˆ ][Ú] vs. [Úffl ]

22

Factor 3: Geography

23

Phonological Alternations Factor 3

[1ˆ ] vs. [1] [E] vs. [Eˆ ] [Ú] vs. [Úffl ]

• Only the [1ˆ ] vs. [1] distinction seems to pick out West Virginiaas opposed to Virginia, North Carolina, Maryland, andDelaware.

24

Noncontrasting Vowels (in Factor Analysis)

he died with1 April2 seven2 kitchen1 Chicago3he died with3 France1 twelve1 January2 Louisiana3New England2 Missouri3 bureau1 St. Louis1 February1Sunday week3 attic2 ten1 second2 all at once1half past seven1 backlog1 bottom2 froze over1 Alabama2what time is it1 chimney1 driven1 dry spell1 dry spell2New Orleans2 fourteen2 broom1 froze over2 Tennessee3half past seven2 eleven2 mantel1 hog pen2 Charleston2Sunday before last5 my wife2 night1 northeast2 northwest2steady drizzle1 quilt1 rose1 second1 a little ways2twenty-seven1 seventy1 sofa1 tomorrow1 Washington3twenty-seven2 three1 pallet1 January1 Baltimore1twenty-seven3 thirteen2 twenty1 wardrobe2 bureau2white ashes2

25

Tentative Conclusions

• Linguistic structure is exploited in dialectal distinctions. Forexample, phonemic distinctions are consistent across lexicalitems.

• Factor analysis effectively identifies linguistic structure inmass comparision

• The technique is enabled by the numeric measure of distancebetween segments.

• Total explained variance is low, only 36% in the first threefactors. Data is noisy.

• Some factors link non-trivial linguistic variations, e.g., [@] vs.[1] on the one hand with [E] vs. [Eˆ ] on the other

26

Future Work

• Identifying which variations to focus on (e.g., [@] vs. [1]) wrt agiven factor is subjective. Can we systematize this?

• Can this technique suggest deeper linguistic relationships,e.g., different concrete alternations that are loaded for thesame factor?

• Are there more general, e.g., data-mining techniques, thatcould be used to probe in data for which no numericalmeasure of difference has been established?

27