Learning Local Phonological Rules -...

Learning LocalPhonological Rules

Jane Chandlee andCesar Koirala

Introduction

Restricting theHypothesis Space

Algorithm

Demonstration

Conclusions

Learning Local Phonological Rules

Jane Chandlee and Cesar Koirala

University of Delaware

Penn Linguistics Colloquium 2013



Introduction


Algorithm

Demonstration

Conclusions

Phonological mappings

(1) German Final Devoicing

a. /ba:d/ 7→ [ba:t], ‘bath’b. /za:g/ 7→ [za:k], ‘say’



Introduction


Algorithm

Demonstration

Conclusions


(2) [+voice, -son] ⇒ [-voice] / #

(3) *[+voice, -son]# >> IDENT(voice)

(4) (ba:d, ba:t), (za:g, za:k),...



Introduction


Algorithm

Demonstration

Conclusions

Main objective

I Present and demonstrate an algorithm for the learningof phonological mappings that exploits a property(i.e. locality) of the linguistic data itself.



Introduction


Algorithm

Demonstration

Conclusions


0

N,V,T1D:λ

#:λ?:D?

#:T

V = vowelN = sonorant consonantD = voiced obstruentT = voiceless obstruent



Introduction


Algorithm

Demonstration

Conclusions

Locality in phonology

‘Normally, phonological rules do not count past two (whichcan be construed as not counting at all but simply examiningone item in strict adjacency to another)’(Kenstowicz, 1994, p. 372).



Introduction


Algorithm

Demonstration

Conclusions

Learning in phonology

I Parameter setting (Gibson and Wexler 1994)

I SPE-style rules (Johnson, 1984; Gildea and Jurafsky, 1996;

Albright and Hayes, 2002, 2003)

I Optimality Theory (Tesar and Smolensky, 1993; Tesar, 1995,

1998; Tesar and Smolensky, 1998; Alderete et al., 2005; Boersma,

1997; Boersma and Hayes, 2001; Hayes, 2004; Pater and Tessier,

2003; Prince and Tesar, 2004; Pater, 2004; Riggle, 2004, 2006,

2009; Merchant and Tesar, 2008; Magri, 2010, 2012)

I Phonotactic learning (Heinz, 2007, Hayes and Wilson, 2008,

among others)



Introduction


Algorithm

Demonstration

Conclusions

Kaplan and Kay (1994)

REGULAR

A ! B / C _ D



Introduction


Algorithm

Demonstration

Conclusions

Subsequentiality

REGULAR

SUBSEQUENTIAL

A ! B / C _ D

Many phonological processes are subsequential(Chandlee, Athanasopoulou, and Heinz, 2012, Gainor et al. 2012,

Chandlee and Heinz, 2012).



Introduction


Algorithm

Demonstration

Conclusions

OSTIA (Oncina et al., 1993)

I Subsequential functions are identifiable in the limit frompositive data (Oncina et al., 1993).

I OSTIA failed to correctly generalize the English flappingrule (Gildea and Jurafsky, 1996).

I The characteristic sample necessary for the algorithm’ssuccess is not present in a natural language dictionary.



Introduction


Algorithm

Demonstration

Conclusions

Using locality

REGULAR

SUBSEQUENTIAL

A ! B / C _ D



Introduction


Algorithm

Demonstration

Conclusions

Strictly Local Stringsets

String membership in a Strictly k-Local stringset can bedetermined by checking the substrings of length k (Rogers and

Pullum, 2011).

(5) a. [mb], [nt], [Nk],...b. *[nb], *[mt], *[Np],...



Introduction


Algorithm

Demonstration

Conclusions

Learning SL stringsets (Garcia et al. 1990, Heinz 2007)

1. Prefix tree

2. State merging (Biermann and Feldman, 1972; Angluin, 1982;

Hopcroft et al., 2001)



Introduction


Algorithm

Demonstration

Conclusions

Prefix tree

{thin, think, thank, thumb, number}

λ

θ

n

ɪ

æ

ʌ

ʌ

n

ŋ

ŋ

m

k

k

m b ɚ



Introduction


Algorithm

Demonstration

Conclusions

State merging

{ aa } { aa∗ }

0 1a 2a 0 1,2a

a



Introduction


Algorithm

Demonstration

Conclusions

Prefix tree transducer

θ:θ

n:n

ɪ:ɪ

æ:æ

ʌ:ʌ

ʌ:ʌ

n:n

n:ŋ

n:ŋ

m:m

k:k

k:k

n:m b:b ɚ:ɚ



Introduction


Algorithm

Demonstration

Conclusions

State merging

Merge states in the prefix tree with the same k-1 lengthsuffix on the input side of the incoming transition.



Introduction


Algorithm

Demonstration

Conclusions

State merging

!:!

n:n

ɪ:ɪ

æ:æ

ʌ:ʌ

ʌ:ʌ

n:n

n:"

n:"

m:m

k:k

k:k

n:m b:b ɚ:ɚ



Introduction


Algorithm

Demonstration

Conclusions

Learning German Final Devoicing

Data

I CELEX German lemmas

I 51,723 underlying-surface pairs: underlying final voicedobstruent posited when represented orthographically

I (...(ba:d, ba:t)...(grIf, grIf)...(sEg, sEk)...(za:g,za:k)...(zIx, zIx)...)

I (...(DVD, DVT)...(DNVT, DNVT)...(TVD,TVT)...(DVD, DVT)...(DVD, DVT)...(DVT, DVT)...)



Introduction


Algorithm

Demonstration

Conclusions

Learning German Final Devoicing

0

VV:V

D

D:D

TT:T

N

N:N

V:V

D:λ

T:T

N:N

#:λ

V:DV

D:DD

T:DT

N:DN

#:T

V:V D:λ T:TN:N

#:λ

V:V

D:λ

T:T

N:N

#:λ



Introduction


Algorithm

Demonstration

Conclusions

Conclusions

I The property of locality is evident in phonologicalmappings regardless of the grammatical formalism usedto describe them.

I We have shown that an algorithm that makes use ofthis property can generalize such mappings frompositive data.

I These findings suggest a reason why, cross-linguistically,phonological processes are restricted in this way.



Introduction


Algorithm

Demonstration

Conclusions

Next steps

I Proof of correctness

I Comparison of other state merging criteria

I Given a phonological mapping, determine whether it canbe learned in this way (i.e., whether it is ‘Strictly Local’)

I Determine the characteristic sample necessary for thealgorithm to succeed.



Introduction


Algorithm

Demonstration

Conclusions

Acknowledgements

Thanks to Jeffrey Heinz, Irene Vogel, and Jim Rogers forvaluable feedback and guidance. And thanks to theorganizers of PLC 37!

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Learning Local Phonological Rules -...

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