International School on Complexity Course on Statistical Physics of Social Dynamics Opinions, Semiotic Dynamics, and Language July 19, 2007. Erice, Italy The Dynamics of Language Evolution [email protected]William S-Y. Wang Chinese University of Hong Kong
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International School on Complexity Course on Statistical Physics of Social Dynamics
International School on Complexity Course on Statistical Physics of Social Dynamics Opinions, Semiotic Dynamics, and Language July 19, 2007. Erice, Italy The Dynamics of Language Evolution [email protected] William S-Y. Wang Chinese University of Hong Kong. - PowerPoint PPT Presentation
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International School on ComplexityCourse on Statistical Physics of Social DynamicsOpinions, Semiotic Dynamics, and LanguageJuly 19, 2007. Erice, Italy
Ma sopra tutte le invenzioni stupende, qual eminenza di mente fu quella di colui che s'immaginò di trovar modo di comunicare i suoi più reconditi pensieri a qualsivoglia altra persona, benché distante per lunghissimo intervallo di luogo e di tempo?
Languages diversify in
space&
time
Major engines for change:vertical, across generations
horizontal, language contact
Leonard BloomfieldLanguage
1933
Cavalli-Sforza, L.L. and W.S-Y.Wang. 1986. Cavalli-Sforza, L.L. and W.S-Y.Wang. 1986. Spatial distance and lexical Spatial distance and lexical replacementreplacement. . LanguageLanguage 62.38-55. 62.38-55.
Cavalli-Sforza, L.L. and W.S-Y.Wang. 1986.Cavalli-Sforza, L.L. and W.S-Y.Wang. 1986.Spatial distance and lexical replacementSpatial distance and lexical replacement. . LanguageLanguage 62.38-55. 62.38-55.
“chaque mot a son histoire”
Given the heterogeneous rates of change exhibited by the features of language, including lexical features, how can we capture statistically their distributions, leading to more substantive understanding?
Meaning : cancellation detour idea acrobatics--------------------------------------------------------------------------------------wago : torikeshi mawarimichi omoitsuki karuwaza和語
1 ashes belly bird bite all earth 2 bark blood claw burn big I 3 cloud bone dog come black name 4 fire breast feather die cold night 5 leaf ear fish drink dry not 6 man egg horn eat fat one 7 moon eye louse fly full road 8 mountain foot tail give good 9 person hair hear green that 10 rain hand kill long this 11 root head know many thou 12 sand heart lie new two 13 seed knee say red we 14 smoke liver see round what 15 star meat sit small who 16 stone mouth sleep warm 17 sun neck stand white 18 tree nose swim yellow 19 water skin walk 20 woman tongue 21 tooth 100 salt year wind 7 9 5 3 2 9 35
Lexical Diffusion - an early modelLexical Diffusion - an early model
UU VV CCWW11 WW11
WW22 WW2 2 ~ ~ WW22
WW33 WW3 3 ~ ~ WW33
.. ,,
.. ..
.. ..
WWnn WWnn
Lexical Diffusion: [u] > [ŭ] in EnglishLexical Diffusion: [u] > [ŭ] in English
UU VV CC-k-k hookhook
-t-t lootloot sosootot footfoot
-d-d foodfood hoodhood
-m-m gloomgloom grogroomom
-n-n noonnoon
Cavalli-Sforza, L.L. & M.W.Feldman. 1981. Cultural Transmission and Evolution. Princeton.
The new word that becomes part of a language, - - -, is an innovation and can be considered as an analog of mutation in biology. - - - When the process of diffusion of an innovation is followed for a sufficiently long time, the frequency of use of the innovation almost always follows an S-shaped curve. At the beginning the number of acceptances rapidly increases - - -. Then follows an approximately linear increase, and finally the increase slows down and is barely perceptible. P.29-30.
• Shen, Zhongwei. 1997. Exploring the Dynamic Aspect of Sound Change. Journal of Chinese Linguistics Monog. #11.
• Niyogi, Partha, 2006. The Computational Nature of Language Learning and Evolution. MIT Press.
Age as Virtual Time
Model for Lexical Diffusion — One Word
u
u (1 – c) u c
c
c
t:
t + t:
u + c = 1
u denotes the proportion of unchanged formsc denotes the proportion of changed forms is a constant that controls the rate of change of c, 0 ≤ ≤ 1
Solution to One Word Model
The frequency of changedforms has the solution:
0 2 4 6 8 10 120
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
t
C (
t)— the Logistic Curve
1e1
e
t
t
tc
Lexical Diffusion and the Snowball Effect
0 2 4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time
Pro
po
rtio
n o
f w
ord
s t
hat
hav
e ac
qu
ired
ch
ang
ed f
orm
Successive words acquirechanged form at an
increasingly rapid rate
i) in the gradient of curvesi) in the gradient of curves
ii) in the separation of curvesii) in the separation of curves
This can be observed:
Three examples of linguistic study:
• Lexical replacement in Micronesia,
• Time depth and prehistory,
• Diffusion of a sound change.
A language is NOT a unitary homogeneous object,
but a heterogeneous, layered, dynamic mosaic of features,
constructed piece-meal by the child during the early years,
and enriched by the adult,
adopting features from other speakers and other languages.
sounds,meanings,words, constructions.etc.
verticaltransmission
horizontaltransmission
How do we best model the two modes of transmission word by word across the lexicon, from generation to generation, from speaker to speaker, and from community to community?
How do we distinguish the products of the two modes of transmission in order to better understand linguistic prehistory, and thereby human prehistory?
Cavalli-Sforza, L.L. and W.S-Y. Wang. 1986. Spatial distance and lexical replacement. Language 62.38-55.
Wang, W.S.-Y., J.Y. Ke and J.W. Minett. 2004. Computational studies of language evolution. 65-106 in Computational Linguistics and Beyond. Huang, C.R. and W. Lenders eds. Academia Sinica: Institute of Linguistics.
Gong, T., J. Ke, J.W. Minett, J.H. Holland and W.S-Y. Wang.
2005. Coevolution of lexicon and syntax from a simulation perspective. Complexity, 10.6: 50-62.
Wang, W.S-Y. and J.W. Minett. 2005. Vertical and horizontal transmission in language evolution. Transactions of the Philological Society 103.2.121-46.
Heartfelt thanks to the two directors,
Luc and Vittorio, and their many able associates
for a week of intellectual feasts on Opinions, Semiotic Dynamics, and Language