how much homophony is normal?cunyphonologyforum.ws.gc.cuny.edu/files/2015/01/kaplanhandout.pdf ·...

How Much Homophony Is Normal?!

Abby KaplanUniversity of California, Santa Cruz

CUNY Conference on the Word in PhonologyJanuary 14, 2010

1 Introduction: Contrast Maintenance in Phonology

Phonological patterns sensitive to the need to maintain contrast

– Dispersion Theory (Flemming 2002): constraints that require contrast amongphonemic categories oppose constraints against expending articulatory e!ort

– Phonological patterns sometimes serve to maximize contrasts among potentiallexical items (Padgett 2003; Padgett and Tabain 2005)

– Some patterns seem especially prone to avoiding neutralization (e.g., lenition,Gurevich (2004))

This sensitivity usually modeled at level of potential lexical items, not actual ones

– Don’t want to predict unattested phonological patterns:

Dispersed inventories only for lexical items in minimal pairs

Rules that apply only where they don’t create homophones

“These questions arise when we take the domain of explanation to bethe set of actual lexical items in a language. But this is in fact notthe practice in generative phonology. Instead, theories model the set ofpossible words of a language....” (Padgett 2003, 78-79)

– Could there be other types of homophony avoidance?

!Many thanks to Aaron Kaplan, Yongeun Lee, Jaye Padgett, Dan Silverman, Paul Willis, and membersof Phlunch at UC Santa Cruz. All shortcomings are my own. This research was funded by an NSF GraduateResearch Fellowship.

1

Silverman (to appear) argues that phonological patterns avoid creating homophonesamong actual words

– Korean has many neutralizing phonological alternations

– But these alternations result in only a relative handful of homophones

– Homophony avoidance in lexical statistics rather than formalization of phonolog-ical pattern

Remaining question: how can we be sure number of homophones in Korean is reallyunexpectedly low?

" Perhaps any set of neutralizations would create very few homophones

To establish a ‘baseline’ level of expected homophony, count homophones produced byalternative phonological rules and compare to actual rule; at least two ways to do this:

1. Hand-select a few alternative rules for comparison

– Silverman’s method; alternatives produce more homophony than actual rules

– Pro: can select phonologically plausible rules that are similar to actual rules

– Con: results highly dependent on which alternatives we happen to pick

2. Compute homophony for large number of alternative rules (‘brute-force’ method)

– Method used for this talk

– Pro: cover a lot more ground than hand-selection approach

– Con: hard to filter out implausible rules

2 Method

2.1 Korean

Why Korean?

– Large number of neutralizing phonological alternations

(1) a. (i) /nAÙ-i/ # [nAÙi] ‘day.nom’(ii) /nAÙ-k’wA/ # [nAtk’wA] ‘day and’

b. (i) /nAÙh-i/ # [nAÙhi] ‘face.nom’(ii) /nAÙh-k’wA/ # [nAtk’wA] ‘face and’

– Writing system roughly morphophonemic: orthography can be used to approxi-mate underlying forms

2

Tab

le1:

Surf

ace

real

izat

ions

ofunder

lyin

gco

da-

onse

tse

quen

ces

inK

orea

n

ph

pp’

tht

t’s

s’Ù

hÙ

Ù’

kh

kk’

mn

lh

$p

ph

p’

pth

pt’

ps’

pÙh

pÙ’

pkh

pk’

pm

pn

pl

ph

pp

hp

hh

ph

ps

ps’

hps’

lplp

hlp

lph

lphh

lph

h

ths’

Ùh

kh

tntl

hh

$t

tht

thth

hth

st’

Ù’sh

ss’

s’h

s’Ù

k’Ùh

ÙÙ

hÙh

hÙh

k

kph

kp’

kth

kt’

ks’

kÙh

kÙ’

kmkn

kl

khk

k’

k’k

hkh

hkh

ks

ksh

ks’

lklk

hlk

mm

ph

mp

mp’

mth

mt

mt’

ms

ms’

mÙh

mÙ

mÙ’

mkh

mk

mk’

mm

mn

ml

mn

np

hnp

np’

nth

ntnt

’ns

ns’

nÙh

nÙ

nÙ’

nkh

nk

nk’

nm

nn

nl

nnh

nhh

nÙ

nÙh

nÙ

NNp

hNp

Np’

Nth

NtNt

’Ns

Ns’

NÙh

NÙNÙ

’Nk

hNk

Nk’

NmNn

NlN

lm

lph

lp

lp’

lth

lt

lt’

ls

ls’

lÙh

lÙ

lÙ’

lkh

lk

lk’

lmln

ll

lml

llh

lhh

lth

lthh

lth

lsls

hls

$p

hp

p’

tht

t’s

s’Ùh

ÙÙ’

khk

k’m

nl

$

3

Table 1 shows surface realizations of underlying coda-onset sequences

– Reflect nine ordered rules from descriptions in Sohn (1994): resyllabification, [h]-aspiration, coda neutralization, sibilation, tensification, consonant cluster simpli-fication, decoronization, [h]-weakening, reduction

– Some other rules suppressed:

Non-neutralizing alternations (e.g., intervocalic voicing)

Morphologically conditioned alternations (e.g., lateralization)

Data on Korean lexicon

– Data from Korean National Database (Lee 2006) (uninflected stems in Koreanorthography; no morpheme boundaries)

– Phonological rules applied via Java scripts (collaboration w/ Paul Willis)

– Rules applied only stem-internally: application to codas may be bled by vowel-initial su"xes, of which there are many (Albright and Kang 2009)

2.2 Procedure

Same procedure for each rule; illustrated here with data for overall neutralizationpattern

2.2.1 Step 1: Count Homophones Created by Rule

Measures of homophony

Homophones Number of words in lexicon w/ at least one homophone

Weighted Homophones Sum of frequencies of words w/ at least one homophone

Homophone Pairs Number of pairs of homophones in lexicon

Homophone Sets Number of (maximal) sets of words in lexicon that all neutralizeto the same thing

Table 2 shows levels of homophony underlyingly and after all rules have applied

" ‘New’: number of homophones added by rules

For results of simulations, only ‘homophones’ and ‘weighted homophones’ shown

" ‘Homophone pairs’ and ‘homophone sets’ very similar to ‘homophones’

4

Table 2: Homophony in Korean lexicon

Measure Underlying Surface NewHomophones 6201 6646 445Weighted Homophones 291681 319835 28154Homophone Pairs 4308 4692 384Homophone Sets 2794 2975 181

2.2.2 Step 2: Simulate Alternative Rules

Three simulations; each creates 1000 random patterns w/ same number of neutraliza-tions as actual rule

‘a’-series simulation: neutralize random sets of coda-onset sequences

– Table 3 illustrates this for overall neutralization pattern

– Randomly distribute onset-coda sequences in table; superimpose grid from actualpattern

– All sequences in same box neutralize with each other

– (Some cells neutralize w/ nothing – left blank; sequences here don’t matter)

– Figure 1 shows results for overall pattern

Histograms show level of homophony across simulated patterns

Vertical lines show actual number of homophones

Title gives percentile rank of actual number of homophones among simulatedpatterns: smaller percentile # actual level of homophony surprisingly low

" For both measures, every simulated pattern resulted in more homophony thanactual pattern

5

Tab

le3:

Exa

mple

ofneu

tral

izat

ion

pat

tern

sin

an‘a

’-se

ries

sim

ula

tion

ÙÙ’

hÙ

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tp’

Ùllh

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s’n

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tht’

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Ùhl

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nhh

psn

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kth

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msl

tht’

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mn

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mm

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llks

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6

Figure 1: Amount of homophony in simulation a

Percentile: 0

# of Homophones

# of

Pat

tern

s

500 1000 1500 2000

015

030

0Percentile: 0

# of Weighted Homophones

# of

Pat

tern

s

30000 50000 70000 90000

010

025

0‘b’-series simulation: randomly shu#e onsets, codas separately

– Table 4 illustrates for overall pattern

– Neutralized sequences more similar than in ‘a’-series: more phonologically plau-sible

– Figure 2 shows results

" Almost every simulated pattern results in more homophony than actual pat-tern

Figure 2: Amount of homophony in simulation b

Percentile: 0

# of Homophones

# of

Pat

tern

s

1000 3000 5000

010

025

0

Percentile: 0.4


# of

Pat

tern

s

40000 80000 120000

010

020

0

7

Tab

le4:

Exa

mple

ofneu

tral

izat

ion

pat

tern

sin

a‘b

’-se

ries

sim

ula

tion

ps’

l$

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ph

nk

Ù’

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hs

k’

ht

p’

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m

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pl

p$

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pp

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ph

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pth

pm

nnp

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nl

n$

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np

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nk

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nt’

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nh

ntnp’

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nth

nm

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llk$

lkÙh

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slk

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tlk

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lkÙ

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lkm

ph

php

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phl

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phÙh

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hp

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plm

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llm$

lmÙh

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hlm

nlm

klm

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slm

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tlm

p’

lmÙ

lmth

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mm

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s’m

lm$

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hm

nm

km

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8

‘c’-series simulation: randomly shu#e segments; combine according to neutralization‘template’ for rule

– Template: specification of which coda-onset sequences neutralize with each other

– Example from tensification rule: for each ordered pair in set B, first memberpreceded by anything from set A neutralizes with second member preceded bysame thing

" So, neutralizing pairs include {k+k, k+k’}, {t+k, t+k’}, {k+t, k+t’}, etc.

Figure 3: Template for tensification

{/A + B1/, /A + B2/}

A = {k, t, p, s, kt, nt, lk, lp, lt, pt, ks,ns, ls, ps}

B = {%k, k’&, %t, t’&, %p, p’&, %s, s’&,%Ù, Ù’&, %sh, s’h&}

– In ‘c’-series simulation: randomly assign new segments to sets A and B (and Cand so on, if template has more sets); restrictions:

Identical segments across sets stay that way (i.e., every [k] in figure 3 becomes[s] in figure 4)

Segments that appear as codas in template must be possible codas, those asonsets must be possible onsets (i.e., every member of set A must be a possiblecoda)

– Example given in figure 4

" New neutralizing pairs include {s+s, s+kh}, {n+s, n+kh}, {s+n, s+s’h}

Figure 4: Example of new sets for tensification template in a ‘c’-series simulation

A = {s, n, t, k, ns, $, N, p, m, ps, ls, lt, pt, lk}

B = {%s, kh&, %n, s’h&, %t, khh&, %k, th&, %t, Nh&, %mh, phh&}

In theory, ‘b’-series simulations phonologically more plausible than ‘a’-series, ‘c’-seriesmore plausible than ‘b’-series

9

3 Results

For each rule, I give:

– Description of rule

– Template for rule

– Homophony produced by rule

– Results of three simulations

Display of simulation results

– Two graphs: homophones measure on left, weighted homophones on right

– Density curves (' smoothed histograms) show distribution of homophony in 1000patterns run in each simulation

– Three curves, one for each simulation (‘a’, ‘b’, ‘c’)

– Vertical bar: actual level of homophony

– Legend notes percentile rank of actual level in each simulation

Note:

– Rules apply in ordered fashion, each rule to output of previous rule

– Possible onsets and codas at each stage di!er accordingly

– ‘Total’ homophones listed for each rule are total homophones in lexicon after ruleapplies

10

3.1 Rule 1: Resyllabification

Resyllabify single coda consonant into following onsetless syllable or syllable with initial[h] (Sohn 1994, 164)

Almost all simulations produce far less homophony than actual rule; most producenone at all!

Small additional peak in histograms for ‘c’-series near actual pattern

" Suggests a single pair of neutralized coda-onset sequences responsible for most ofhomophony produced by resyllabification

Figure 5: Homophony afterresyllabification

Measure Total NewHomophones 6330 129Weighted 295832 4151Pairs 4417 109Sets 2847 53

Figure 6: Template for resyllabification

{/A + B/, /B + A/}

{/C1 + B/, /C2 + C3/}

A = {k, n, t, l, m, p, s, Ù, Ùh, kh,th, ph, h, k’, s’}

B = {$}

C = {%ks, k, s&, %nÙ, n, Ù&, %nh, n,h&, %lk, l, k&, %lm, l, m&, %lp, l, p&,%ls, l, s&, %lth, l, th&, %lph, l, ph&,%lh, l, h&, %ps, p, s&}

Figure 7: Amount of homophony in simulation series 1

0 50 100 150 200

0.00

00.

010

0.02

00.

030

# of Homophones

Dens

ity

a (99)b (99.4)c (99.2)

0 2000 6000 10000

0e+0

04e−0

48e−0

4


Dens

ity

a (97.2)b (99.1)c (98.6)

11

3.2 Rule 2: [h]-Aspiration

Fuse plain noncontinuant obstruent with adjacent [h] into homorganic aspirated ob-struent (Sohn 1994, 166)

In ‘a’-series, most simulations yield less homophony than actual pattern: over half of‘a’-series simulations yield none

In ‘b’- and ‘c’-series, actual level of homophony in lower half of simulations

Note that the more phonologically plausible the simulation, the more homophony ittends to produce (c > b > a)

Figure 8: Homophony after[h]-aspiration


Figure 9: Template for [h]-aspiration

{/A1 + B/, /B + A1/,/C + A2/, /C + A3/}

{/D + A2/, /D + A3/}

A = {%k, kh, kh&, %t, th, th&, %p,ph, ph&, %Ù, Ùh, Ùh&}

B = {h}

C = {$}

D = {k, n, t, l, m, p, s, N, Ù, Ùh,kh, th, ph, k’, s’}


0 200 400 600 800

0.00

00.

004

0.00

8

# of Homophones

Dens

ity

a (86.5)b (36.2)c (22.3)

0 10000 20000 30000

0.00

000

0.00

015


Dens

ity

a (90.4)b (51.4)c (36.6)

12

3.3 Rule 3: Coda Neutralization

Syllable-final obstruents become plain stops; /h/, /s/ # [t] (Sohn 1994, 165)

Simulated patterns yield more homophony than actual pattern; less concentrated atlow end of scale

Exception: weighted homophones measure for ‘a’-series

Note that percentile rank of actual level of homophony is generally greater for weightedhomophones measure

Figure 11: Homophony aftercoda neutralization


Figure 12: Template for coda neutralization

{/A1 + B/, /A2 + B/,/A3 + B}

{/C1 + B/, /C2 + B/,/C3 + B/, /C4 + B/,/C5 + B/, /C6 + B/,

/C7 + B/}

{/D1 + B/, /D2 + B/}

A = {%k, k’, kh&, %ls, lth, lh&}

B = {k, n, t, l, m, p, s, N, $, Ù,Ùh, kh, th, ph, k’, t’, p’, s’, Ù’,nh, lh, mh, sh, Ùhh, khh, thh,phh, hh, k’h, s’h}

C = {%t, s, Ù, Ùh, th, h, s’&}

D = {%p, ph&, %nÙ, nh&, %lp,lph&}


0 1000 2000 3000 4000

0e+0

04e−0

48e−0

4

# of Homophones

Dens

ity

a (4.5)b (11)c (1.4)

−20000 20000 60000 100000

0.00

000

0.00

006

0.00

012


Dens

ity

a (83.3)b (27)c (7.4)

13

3.4 Rule 4: Sibilation

/t/ becomes [s] before [s] or [s’] (Sohn 1994, 165)

Since all /s/s became [t]s in Coda Neutralization, this rule is non-neutralizing

3.5 Rule 5: Tensification

Plain obstruents become tense after obstruents (Sohn 1994, 173)

Most simulated patterns produce more homophony than actual rule

Again, more plausible simulations tend to yield more homophony (c > b > a)

Figure 14: Homophony af-ter tensification


Figure 15: Template for tensification

{/A + B1/, /A + B2/}

A = {k, t, p, s, kt, nt, lk, lp, lt,pt, ks, ns, ls, ps}

B = {%k, k’&, %t, t’&, %p, p’&, %s, s’&,%Ù, Ù’&, %sh, s’h&}


0 500 1000 1500

0.00

000.

0015

0.00

30

# of Homophones

Dens

ity

a (21.8)b (7.4)c (3.2)

−10000 10000 30000 50000

0e+0

04e−0

58e−0

5


Dens

ity

a (15.1)b (5.2)c (2.4)

14

3.6 Rule 6: Consonant Cluster Simplification

Delete one consonant from all coda clusters (Sohn 1994, 170)

– If first consonant in cluster is /l/ and second is non-coronal stop, delete /l/ (thissimplifies the facts somewhat)

– Otherwise, delete second consonant

Most simulated patterns produce far more homophony than actual rule

Again, note that percentile rank for actual rule is greater for weighted homophonesmeasure

Figure 17: Homophony af-ter consonant cluster sim-plification


Figure 18: Template for simplification

{/A1 + B/, /A2 + B/,/A3 + B/, /A4 + B/}

{/C1 + B/, /C2 + B/,/C3 + B/}

A = {%k, kt, lk, ks&, %l, lm, lt, ls&,%p, lp, pt, ps&}

B = {k, n, t, l, m, p, s, N, $, Ù, Ùh,kh, th, ph, h, k’, t’, p’, s’, Ù’, nh,lh, mh, Nh, sh, Ùhh, khh, thh, phh,hh, k’h, s’h}

C = {%n, nt, ns&}


0 1000 2000 3000

0e+0

04e−0

48e−0

4

# of Homophones

Dens

ity

a (0)b (3.9)c (1.1)

0 20000 60000

0e+0

04e−0

58e−0

5


Dens

ity

a (22)b (17.1)c (14.2)

15

3.7 Rule 7: Decoronization

/t/ assimilates in place to a following stop (Sohn 1994, 175)

Decoronization creates practically no homophony (just one pair of words)

Seems to be a lower limit: no simulation produced no homophones

Figure 20: Homophony af-ter decoronization


Figure 21: Template for decoronization

{/A1 + B/, /A2 + B/}

{/A1 + C/, /A3 + C/}

A = {%t, k, p&}

B = {k, N, kh, k’, Nh, khh, k’h}

C = {m, p, ph, p’, mh, phh}


0 100 300 500

0.00

00.

010

0.02

0

# of Homophones

Dens

ity

a (0)b (0)c (0)

0 5000 10000 15000

0e+0

04e−0

48e−0

4


Dens

ity

a (0)b (0)c (0)

16

3.8 Rule 8: [h]-Weakening

Delete /h/ between sonorants

This rule creates more homophones than any other

Actual level of homophony always in top half of simulations

Recurrence of two patterns

– Phonologically more plausible simulations yield more homophony

– Percentile rank of actual homophony greater for weighted homophones measure

Figure 23: Homophony af-ter [h]-weakening


Figure 24: Template for [h]-weakening

{/A + C1/, /A + C2/}

{/B + C1/, /B + C2/}

{/B + D1/, /B + D2/}

A = {k, t, p, s, n, l, m, N}

B = {$}

C = {%n, nh&, %N, Nh&, %l, lh&, %m,mh&}

D = {%$, h&}


0 500 1000

0.00

00.

004

0.00

8

# of Homophones

Dens

ity

a (98.4)b (77.5)c (53.7)

−10000 0 10000 30000

0.00

000

0.00

015

0.00

030


Dens

ity

a (99.1)b (88.2)c (72.2)

17

3.9 Rule 9: Pre-Tense/Aspirate Reduction

Delete plain obstruents before homorganic tense or aspirated obstruents (Sohn 1994,175)

Actual level of homophony seems to fall in about the middle of simulated patterns

Many simulations create no homophony

Again, note greater percentile rank for actual level for weighted homophones measure

Figure 26: Homophony af-ter pre-tense/aspirate re-duction


Figure 27: Template for pre-tense/aspirate reduction

{/A + B/, /C + B/}

{/D + E/, /C + E/}

{/F + G/, /C + G/}

A = {k}

B = {kh, k’, khh, k’h}

C = {$}

D = {t, s}

E = {Ùh, th, t’, s’, Ù’, Ùhh, thh,s’h}

F = {p}

G = {ph, p’, phh}


0 200 400 600 800

0.00

00.

004

0.00

80.

012

# of Homophones

Dens

ity

a (40.4)b (46)c (43.9)

−5000 0 5000 15000

0e+0

02e−0

44e−0

4


Dens

ity

a (76.2)b (67.2)c (67.8)

18

4 Discussion

4.1 Does Korean Have Low Homophony?

Rules 2, 3, 5, 6, and 7 produce less homophony than simulated patterns, as does overallpattern

But rules 1, 8, and 9 produce more

To assess overall results across simulations: linear mixed-e!ects model predicting per-centile rank of actual level of homophony for each rule/measure

– Predictors: measure of homophony, simulation series

– Random e!ect of rule

Two models: one for raw percentiles, one for arcsin-transformed percentiles

" Similar results; only raw percentiles presented here

Results

– Intercept: 40

" Below 50: suggests rules yield less homophony than the median, but notsignificantly di!erent from 50

– Weighted homophones measure associated with significantly greater percentilesthan other measures (p = .011)

– Percentile rank for ‘b’-series simulations significantly less than for ‘a’-series (p =.0014), for ‘c’-series significantly less than for ‘b’-series (p = .0000014)

Tentative conclusion: actual level of homophony is indeed low

– Low intercept in model

– More rules yield less homophony than their simulations than yield more

– Overall pattern has less homophony than simulations

4.2 Possible Mechanisms of Homophony Avoidance

As discussed above, we don’t want to build homophony avoidance (as opposed toneutralization avoidance) into formal phonological patterns

These results suggest only a gradient avoidance of homophony

How might this situation come about? Possibilities:

19

1. Given phonetic precursor to a rule, the less homophony the rule would create, themore likely the precursor is to be phonologized

2. Rules tend to neutralize contrasts that are already perceptually suboptimal; lexi-con is already optimized to avoid homophones based on hard-to-perceive contrasts

3. Words in dense neighborhoods tend to resist alternation (Ussishkin and Wedel2009)

4.3 Other Patterns in the Data

The more phonologically plausible the rule, the more homophony it creates

– If true, this trend might argue against explanation 2 above

– Caveat: most simulated rules still not very plausible

– Future research: how to better filter rules for phonological plausibility?

Level of actual homophony looks less surprisingly low when homophones are weightedby frequency

– In other words, few words are homophonized, but they tend to be especiallyfrequent

– Possible explanation: short words more likely both to be homophonized and tobe frequent

" Unlikely: should be just as true of simulated patterns as of actual patterns

– Looks like an anti-functional tendency

5 Conclusion

Neutralizing alternations in Korean appear to produce less homophony than expected

Thus, phonological rules may be sensitive to contrast among actual words, not justpotential ones

References

Adam Albright and Yoonjung Kang. Predicting innovative alternations in Korean verbparadigms. In Current Issues in Unity and Diversity of Languages: Collection of thePapers Selected from the CIL 18, Held at Korea University in Seoul, pages 1–20, Seoul,Korea, 2009. Linguistic Society of Korea.

Edward S. Flemming. Auditory Representations in Phonology. Outstanding Dissertations inLinguistics. Routledge, New York, NY, 2002.

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Naomi Gurevich. Lenition and Contrast: The Functional Consequences of Certain Phono-logically Conditioned Sound Changes. Outstanding Dissertations in Linguistics. GarlandPublishing, New York, NY, 2004.

Yongeun Lee. Sub-Syllabic Constituency in Korean and English. PhD thesis, NorthwesternUniversity, 2006.

Jaye Padgett. Contrast and post-velar fronting in Russian. Natural Language and LinguisticTheory, 21(1):39–87, 2003.

Jaye Padgett and Marija Tabain. Adaptive dispersion theory and phonological vowel reduc-tion in Russian. Phonetica, 62:14–54, 2005.

Daniel Silverman. Neutralzation and anti-homophony in Korean. Journal of Linguistics, toappear.

Ho-Min Sohn. Korean. Descriptive Grammars. Routledge, New York, NY, 1994.

Adam Ussishkin and Andrew Wedel. Lexical access, e!ective contrast, and patterns in thelexicon. Ms., University of Arizona, 2009.

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