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Non-Tree-Like Processes in Language Evolution

Non-Tree-Like Processes in Language Evolution

Talk, held at the EVOLUNET Summer School on Networks (Roscoff, 2016/07/04-08).

Johann-Mattis List

July 08, 2016
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  1. Non-Tree-Like Processes in Language Evolution Johann-Mattis List DFG research fellow

    Centre des recherches linguistiques sur l’Asie Orientale Team Adaptation, Integration, Reticulation, Evolution EHESS and UPMC, Paris 2016/07/08 1 / 52
  2. iPod walkman "All languages change, as long as they exist."

    (August Schleicher 1863) Prolog 2 / 52
  3. iPod Indo-European Germanic Old English English p f f f

    ə a æ ɑː t d d ð eː eː e ə r r r r walkman "All languages change, as long as they exist." (August Schleicher 1863) Prolog 2 / 52
  4. iPod Indo-European Germanic Old English English p f f f

    ə a æ ɑː t d d ð eː eː e ə r r r r walkman L₁ "All languages change, as long as they exist." (August Schleicher 1863) Prolog 2 / 52
  5. iPod Indo-European Germanic Old English English p f f f

    ə a æ ɑː t d d ð eː eː e ə r r r r walkman L₁ L₁ L₁ L₁ L₁ "All languages change, as long as they exist." (August Schleicher 1863) Prolog 2 / 52
  6. iPod Indo-European Germanic Old English English p f f f

    ə a æ ɑː t d d ð eː eː e ə r r r r walkman L₁ L₁ L₁ L₁ L₁ "All languages change, as long as they exist." (August Schleicher 1863) Prolog 2 / 52
  7. iPod Indo-European Germanic Old English English p f f f

    ə a æ ɑː t d d ð eː eː e ə r r r r walkman L₁ L₁ L₁ "All languages change, as long as they exist." (August Schleicher 1863) Prolog 2 / 52
  8. iPod Indo-European Germanic Old English English p f f f

    ə a æ ɑː t d d ð eː eː e ə r r r r walkman L₂ L₁ L₃ "All languages change, as long as they exist." (August Schleicher 1863) Prolog 2 / 52
  9. Prolog Languages What is a Language? Norwegian, Swedish, and Danish

    are different languages . . Běijīng-Chinese, Shànghǎi-Chinese und Hakka-Chinese are dialects of the same language 5 / 52
  10. Prolog Languages What is a Language? Běijīng Chinese 1 iou²¹

    i⁵⁵ xuei³⁵ pei²¹fəŋ⁵⁵ kən⁵⁵ tʰai⁵¹iaŋ¹¹ t͡ʂəŋ⁵⁵ ʦai⁵³ naɚ⁵¹ t͡ʂəŋ⁵⁵luən⁵¹ Hakka Chinese 1 iu³³ it⁵⁵ pai³³a¹¹ pet³³fuŋ³³ tʰuŋ¹¹ ɲit¹¹tʰeu¹¹ hɔk³³ e⁵³ au⁵⁵ Shànghǎi Chinese 1 ɦi²² tʰɑ̃⁵⁵ ʦɿ²¹ poʔ³foŋ⁴⁴ taʔ⁵ tʰa³³ɦiã⁴⁴ ʦəŋ³³ hɔ⁴⁴ ləʔ¹lə²³ʦa⁵³ Běijīng Chinese 2 ʂei³⁵ də⁵⁵ pən³⁵ liŋ²¹ ta⁵¹ Hakka Chinese 2 man³³ ɲin¹¹ kʷɔ⁵⁵ vɔi⁵³ Shànghǎi Chinese 2 sa³³ ɲiŋ⁵⁵ ɦəʔ²¹ pəŋ³³ zɿ⁴⁴ du¹³ Norwegian 1 nuːɾɑʋinˑn̩ ɔ suːln̩ kɾɑŋlət ɔm Swedish 1 nuːɖanvɪndən ɔ suːlən tv̥ɪstadə ən gɔŋ ɔm Danish 1 noʌ̯ʌnvenˀn̩ ʌ soːl̩ˀn kʰʌm eŋg̊ɑŋ i sd̥ʁiðˀ ʌmˀ Norwegian 2 ʋem ɑ dem sɱ̩ ʋɑː ɖɳ̩ stæɾ̥kəstə Swedish 2 vɛm ɑv dɔm sɔm vɑ staɹkast Danish 2 vɛmˀ a b̥m̩ d̥ vɑ d̥n̩ sd̥æʌ̯g̊əsd̥ə 6 / 52
  11. Prolog Languages What is a Language? From the perspective of

    the lexicon and the sound system, the Chinese dialects are at least as diverse as the Scandi- navian languages 6 / 52
  12. Prolog Languages Language as a Diasystem Languages are complex aggregates

    of different linguistic systems which “coexist and mutually influence each other” (Coseriu 1973: 40, my translation). . . 7 / 52
  13. Prolog Languages Language Variation: Complexity of Borrowing expected Mandarin [ma₅₅po₂₁lou]

    attested Mandarin [wan₅₁paw₂₁lu₅₁] explanation Cantonese [maːn₂₂pow₃₅low₃₂] 9 / 52
  14. Prolog Languages Language Variation: Complexity of Borrowing English Cantonese Mandarin

    maːlboʁo maːn22 pow35 low32 wan51 paw21 lu51 Proper Name “Road of 1000 Tre- asures” “Road of 1000 Tre- asures” 万宝路 10 / 52
  15. Prolog Language History Modeling Language History: Dendrophilia August Schleicher (1821-1868)

    “These assumptions, which follow logically from the results of our re- search, can be best illustrated by the image of a branching tree.” (Schle- icher 1853: 787) 11 / 52
  16. Prolog Language History Modeling Language History: Dendrophobia Johannes Schmidt (1843-1901)

    “You can turn it as you want, but as long as you stick to the idea that the his- torically attested languages have been developing by multiple furcations of an ancestral language, that is, as long as you assume that there is a Stammbaum [family tree] of the Indo-European lan- guages, you will never be able to explain all facts which have been assembled in a scientifically satisfying way.” (Schmidt 1872: 17, my translation) 13 / 52
  17. Prolog Language History Modeling Language History: Dendrophobia Johannes Schmidt (1843-1901)

    “I want to replace [the tree] by the im- age of a wave that spreads out from the center in concentric circles be- coming weaker and weaker the far- ther they get away from the center.” (Schmidt 1872: 27, my translation) 14 / 52
  18. Prolog Language History Modeling Language History: Networks Trees are bad,

    because... they are difficult to reconstruct............ 16 / 52
  19. Prolog Language History Modeling Language History: Networks Trees are bad,

    because... they are difficult to reconstruct............ languages do not always split............ .......... ............ ............ 16 / 52
  20. Prolog Language History Modeling Language History: Networks Trees are bad,

    because... they are difficult to reconstruct............ languages do not always split............ .......... ............ ............ they are boring, since they only model the vertical aspects of language history ............ 16 / 52
  21. Prolog Language History Modeling Language History: Networks Trees are bad,

    because... they are difficult to reconstruct............ languages do not always split............ .......... ............ ............ they are boring, since they only model the vertical aspects of language history ............ Waves are bad, because nobody knows how to reconstruct them 16 / 52
  22. Prolog Language History Modeling Language History: Networks Trees are bad,

    because... they are difficult to reconstruct............ languages do not always split............ .......... ............ ............ they are boring, since they only model the vertical aspects of language history ............ Waves are bad, because nobody knows how to reconstruct them languages still diverge, even if not necessarily in split processes 16 / 52
  23. Prolog Language History Modeling Language History: Networks Trees are bad,

    because... they are difficult to reconstruct............ languages do not always split............ .......... ............ ............ they are boring, since they only model the vertical aspects of language history ............ Waves are bad, because nobody knows how to reconstruct them languages still diverge, even if not necessarily in split processes they are boring, since they only model the horizontal aspects of language history 16 / 52
  24. Prolog Language History Modeling Language History: Networks Hugo Schuchardt (1842-1927)

    “We connect the branches and twigs of the tree with countless horizon- tal lines and it ceases to be a tree.” (Schuchardt 1870 [1900]: 11) 17 / 52
  25. Biological Approaches in Historical Linguistics Keys to the Past Keys

    to the Past The Geological Evidences of The Antiquity of Man with Remarks on Theories of The Origin of Species by Variation By Sir Charles Lyell London John Murray, Albemarle Street 1863 20 / 52
  26. Biological Approaches in Historical Linguistics Keys to the Past Keys

    to the Past If we new not- hing of the existence of Latin, - if all historical documents previous to the fin- teenth century had been lost, - if tra- dition even was si- lent as to the former existance of a Ro- man empire, a me- re comparison of the Italian, Spanish, Portuguese, French, Wallachian, and Rhaetian dialects would enable us to say that at some time there must ha- ve been a language, from which these six modern dialects derive their origin in common. 20 / 52
  27. Biological Approaches in Historical Linguistics Keys to the Past Keys

    to the Past: Uniformitarianism (C. Lyell) 21 / 52
  28. Biological Approaches in Historical Linguistics Keys to the Past Keys

    to the Past: Uniformitarianism (C. Lyell) Uniformity of Change: Laws of change are uniform. They have applied in the past as they apply now and will apply in the future, no matter at which place. 21 / 52
  29. Biological Approaches in Historical Linguistics Keys to the Past Keys

    to the Past: Uniformitarianism (C. Lyell) Uniformity of Change: Laws of change are uniform. They have applied in the past as they apply now and will apply in the future, no matter at which place. Graduality of Change: Change proceeds gradually, not abrupt. 21 / 52
  30. Biological Approaches in Historical Linguistics Keys to the Past Keys

    to the Past: Uniformitarianism (C. Lyell) Uniformity of Change: Laws of change are uniform. They have applied in the past as they apply now and will apply in the future, no matter at which place. Graduality of Change: Change proceeds gradually, not abrupt. Abductive Reasoning: We can infer past events and processes by investigating patterns observed in the present, which becomes the “key to the interpretation of some mystery in the archives of remote ages” (Lyell 1830: 165) 21 / 52
  31. Biological Approaches in Historical Linguistics Keys to the Past Keys

    to the Past: Uniformitarianism (A. Schleicher) Language Change is a gradual process (Schleicher 1848: 25). is a law-like process (Schleicher 1848: 25). is a natural process which occurs in all languages (Schleicher 1848: 25). universal process which occurs in all times (Schleicher 1863[1873]: 10f). allows us to infer past processes and extinct languages by investigating the languages of the present (see Schleicher 1848: 25). 22 / 52
  32. Biological Approaches in Historical Linguistics Keys to the Past Keys

    to the Past: Summary It was not the direct exchange of ideas that lead to the de- velopment of similar approaches in biology and linguistics, but the astonishing fact that scholars in both fields would at about the same time detect striking parallels between both disciplines, both regarding their theoretical founda- tions and the processes they were investigating. 23 / 52
  33. Biological Approaches in Historical Linguistics Keys to the Past Keys

    to the Past: Summary It was not the direct exchange of ideas that lead to the de- velopment of similar approaches in biology and linguistics, but the astonishing fact that scholars in both fields would at about the same time detect striking parallels between both disciplines, both regarding their theoretical founda- tions and the processes they were investigating. And linguists were the first to draw trees! 23 / 52
  34. Biological Approaches in Historical Linguistics Keys to the Past Keys

    to the Past: Summary 1700 1800 1750 1850 List et al. (in preparation) Stiernhielm's Lingua Nova 1671 Gallet's Arbre ca. 1800 Darwin's Origins 1859 De Buffon's Table 1755 Schleicher's Stammbaum 1853 Darwin's Tree Sketch 1837 Lamarck's Tableaux 1809 Čelakovský's Rodový Kmen 1853 Rühling's Tabula 1774 Hicke's Affinitas 1689 Schottels's Tabelle 1663 24 / 52
  35. Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative

    Turn “Indo-European and computational cladistics” (Ringe, Warnow and Taylor 2002) “Language-tree divergence times support the Anatolian theory of Indo-European origin” (Gray und Atkinson 2003) “Language classification by numbers” (McMahon und McMahon 2005) “Curious Parallels and Curious Connections: Phylogenetic Thinking in Biology and Historical Linguistics” (Atkinson und Gray 2005) “Automated classification of the world’s languages” (Brown et al. 2008) “Indo-European languages tree by Levenshtein distance” (Serva and Petroni 2008) “Networks uncover hidden lexical borrowing in Indo-European language evolution” (Nelson-Sathi et al. 2011) 26 / 52
  36. Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative

    Turn “Indo-European and computational cladistics” (Ringe, Warnow and Taylor 2002) “Language-tree divergence times support the Anatolian theory of Indo-European origin” (Gray und Atkinson 2003) “Language classification by numbers” (McMahon und McMahon 2005) “Curious Parallels and Curious Connections: Phylogenetic Thinking in Biology and Historical Linguistics” (Atkinson und Gray 2005) “Automated classification of the world’s languages” (Brown et al. 2008) “Indo-European languages tree by Levenshtein distance” (Serva and Petroni 2008) “Networks uncover hidden lexical borrowing in Indo-European language evolution” (Nelson-Sathi et al. 2011) 26 / 52
  37. Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative

    Turn: Words as Genes Basic Concept German ID English ID Italian ID French ID HAND Hand 1 hand 1 mano 2 main 2 BLOOD Blut 3 blood 3 sangue 4 sang 4 HEAD Kopf 5 head 6 testa 7 tête 7 TOOTH Zahn 8 tooth 8 dente 8 dent 8 TO SLEEP schlafen 9 sleep 9 dormir 10 dormir 10 TO SAY sagen 11 say 11 dire 12 dire 12 ... ... ... ... ... ... ... ... ... 27 / 52
  38. Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative

    Turn: Words as Genes Basic Concept German ID English ID Italian ID French ID HAND Hand 1 hand 1 mano 2 main 2 BLOOD Blut 3 blood 3 sangue 4 sang 4 HEAD Kopf 5 head 6 testa 7 tête 7 TOOTH Zahn 8 tooth 8 dente 8 dent 8 TO SLEEP schlafen 9 sleep 9 dormir 10 dormir 10 TO SAY sagen 11 say 11 dire 12 dire 12 ... ... ... ... ... ... ... ... ... 27 / 52
  39. Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative

    Turn: Words as Genes ID Proto-Form Basic Concept German English Italian French 1 PGM *xanda- HAND 1 1 0 0 2 LAT mānus HAND 0 0 1 1 3 PGM *blođa- BLOOD 1 1 0 0 4 LAT sanguis BLOOD 0 0 1 1 5 PGM *kuppa- HEAD 1 0 0 0 6 PGM *xawbda- HEAD 0 1 0 0 7 LAT tēsta HEAD 0 0 1 1 8 PIE *h3 dont- TOOTH 1 1 1 1 9 PGM *slēpan- TO SLEEP 1 1 0 0 10 LAT dormīre TO SLEEP 0 0 1 1 11 PGM *sagjan- TO SAY 1 1 0 0 12 LAT dīcere TO SAY 0 0 1 1 ... ... ... ... ... ... ... 27 / 52
  40. Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative

    Turn: Words as Genes English 111 German 101 French 000 Italian 001 101 001 001 + B − C + A Char. English German French Italian A 1 1 0 0 B 1 0 0 0 C 1 1 0 1 27 / 52
  41. Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative

    Turn: Sounds as Nuclein Bases Concept German English Italian French “HAND” G E I F Hand 0 1 2 3 hand 1 0 2 3 mano 2 2 0 2 main 3 3 2 0 “BLOOD” G E I F Blut 0 4 5 4 blood 4 0 6 5 sangue 5 6 0 2 sang 4 5 2 0 Edit Distances between Orthographic Entries 28 / 52
  42. Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative

    Turn: Sounds as Nuclein Bases German English Italian French German 0 30 60 55 English 30 0 60 50 Italian 60 60 0 20 French 55 50 20 0 28 / 52
  43. Biological Approaches in Historical Linguistics Analogies and Parallels Analogies and

    Parallels Parallels between Species and Languages (Pagel 2009) aspect species languages unit of replication gene word replication asexual und sexual reproduction learning speciation cladogenesis language split forces of change natural selection and genetic drift social selection and trends differentiation tree-like tree-like 29 / 52
  44. Biological Approaches in Historical Linguistics Analogies and Parallels Analogies and

    Parallels Differences between Species and Languages (Geisler & List 2013) Aspect Species Languages domain Popper’s World I Popper’s World III relation between form and function mechanical arbitrary origin monogenesis unclear sequence similarity universal (indepen- dent of species) language-specific differentiation tree-like network-like 31 / 52
  45. Non-Tree-Like Processes in Language Evolution Background Background Organizational Complexity in

    Biological Evolution (E. Bapteste) multi-agent multi-lineages multi-levels nested interconnected 34 / 52
  46. Non-Tree-Like Processes in Language Evolution Background Background Organizational Complexity in

    Language Evolution multi-agent → yes! e.g., dimensions of variation multi-lineages → yes! e.g., “chaque mot a son histoire” multi-levels → yes! e.g., levels of grammar nested → yes! e.g., syntax, morphology interconnected → yes! e.g., lexicon and phonology 34 / 52
  47. Non-Tree-Like Processes in Language Evolution Background Background 1 sound change

    (no parallel with biology) 2 semantic change (no parallel with biology) 3 word formation (protein assembly) 35 / 52
  48. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa 36 / 52
  49. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa l > j 36 / 52
  50. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j 36 / 52
  51. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j l > l 36 / 52
  52. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j l > l l > j / p _ 36 / 52
  53. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j l > l l > j / p _ Not sounds change, sound systems change (Bloomfield 1933)! 36 / 52
  54. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j l > l l > j / p _ Not sounds change, sound systems change (Bloomfield 1933)! Sound change depends on the context in which the sounds occur! 36 / 52
  55. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j l > l l > j / p _ Not sounds change, sound systems change (Bloomfield 1933)! Sound change depends on the context in which the sounds occur! Sound change largely follows irreversible patterns! 36 / 52
  56. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Cognate List Alignment Correspondence List German dünn d ʏ n GER ENG Frequ. d θ 3 x d d 1 x n n 1 x m m 1 x ŋ ŋ 1 x English thin θ ɪ n German Ding d ɪ ŋ English thing θ ɪ ŋ German dumm d ʊ m English dumb d ʌ m German Dorn d ɔɐ n English thorn d ɔː n 37 / 52
  57. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Cognate List Alignment Correspondence List German dünn d ʏ n GER ENG Frequ. d θ 3 x d d 1 x n n 1 x m m 1 x ŋ ŋ 1 x English thin θ ɪ n German Ding d ɪ ŋ English thing θ ɪ ŋ German dumm d ʊ m English dumb d ʌ m German Dorn d ɔɐ n English thorn d ɔː n 37 / 52
  58. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Cognate List Alignment Correspondence List German dünn d ʏ n GER ENG Frequ. d θ 2 x d d 1 x n n 1 x m m 1 x ŋ ŋ 1 x English thin θ ɪ n German Ding d ɪ ŋ English thing θ ɪ ŋ German dumm d ʊ m English dumb d ʌ m German Dorn d ɔɐ n English thorn d ɔː n 37 / 52
  59. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Cognate List Alignment Correspondence List German dünn d ʏ n GER ENG Frequ. d θ 2 x d d 1 x n n 1 x m m 1 x ŋ ŋ 1 x English thin θ ɪ n German Ding d ɪ ŋ English thing θ ɪ ŋ German dumm d ʊ m English dumb d ʌ m German Dorn d ɔɐ n English thorn θ ɔː n 37 / 52
  60. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Cognate List Alignment Correspondence List German dünn d ʏ n GER ENG Frequ. d θ 3 x d d 1 x ? n n 2 x m m 1 x ŋ ŋ 1 x English thin θ ɪ n German Ding d ɪ ŋ English thing θ ɪ ŋ German dumm d ʊ m English dumb d ʌ m German Dorn d ɔɐ n English thorn θ ɔː n 37 / 52
  61. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Cognate List Alignment Correspondence List German dünn d ʏ n GER ENG Frequ. d θ 3 x d d 1 x n n 2 x m m 1 x ŋ ŋ 1 x English thin θ ɪ n German Ding d ɪ ŋ English thing θ ɪ ŋ German dumm d ʊ m English dumb d ʌ m German Dorn d ɔɐ n English thorn θ ɔː n 37 / 52
  62. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Background

    Cognate List Alignment Correspondence List German dünn d ʏ n GER ENG Frequ. d θ 3 x n n 2 x ŋ ŋ 1 x English thin θ ɪ n German Ding d ɪ ŋ English thing θ ɪ ŋ German Dorn d ɔɐ n English thorn θ ɔː n German dumm d ʊ m English dumb d ʌ m 37 / 52
  63. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Characteristics

    • universal • language-specific • limited • widely varying 38 / 52
  64. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Characteristics

    • universal • language-specific • limited • widely varying • constant • mutable 38 / 52
  65. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Investigation

    A little experiment... data from 8 Bai dialects (Sino-Tibetan language spoken in China, Allen 2007) cognate (homologous) parts in all words were aligned from the sounds, a network was reconstructed, showing the frequency in which homologous sounds occur in the same column of an alignment the network was further clustered using Markov clustering (Dongen 2002) for community structure 39 / 52
  66. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Investigation

    Luobenzhuo.tɕʰ.T Yunlong.tɕʰ.T Luobenzhuo.tʂʰ.T Eryuan.tɕʰ.T Qiliqiao.tʃʰ.T Luobenzhuo.ʈʰ.T Yunlong.tsʰ.T Xiangyun.tsʰ.T Jianchuan.k.K Heqi.k.K Xiangyun.k.K Xiangyun.z.S Yunlong.k.K Xiangyun.kʲ.K Qiliqiao.k.K Jianchuan.kʰ.K Eryuan.kʰ.K Lanping.kʰ.K Heqi.kʰ.K Luobenzhuo.kʰ.K Jianchuan.tɕ.T Eryuan.kʲ.K Luobenzhuo.z.S Xiangyun.kʰ.K Qiliqiao.kʰ.K Yunlong.kʰ.K Luobenzhuo.c.K Eryuan.kʰʲ.K Heqi.tɕ.T Qiliqiao.tʃ.T Luobenzhuo.dʐ.T Eryuan.tɕ.T Lanping.tɕ.T Luobenzhuo.tʂ.T Jianchuan.ts.T Eryuan.tʂ.T Yunlong.dz.T Eryuan.ts.T Qiliqiao.tɕ.T Yunlong.tɕ.T Lanping.ts.T Qiliqiao.tɕʰ.T Luobenzhuo.tsʰ.T Yunlong.dʑ.T Heqi.tsʰ.T Lanping.tɕʰ.T Xiangyun.tɕʰ.T Eryuan.tsʰ.T Xiangyun.tɕ.T Jianchuan.tɕʰ.T Heqi.ts.T Heqi.tɕʰ.T Jianchuan.tsʰ.T Xiangyun.ts.T Yunlong.ts.T Eryuan.z.S Luobenzhuo.ʑ.S Heqi.j.J Qiliqiao.ɲ.N Xiangyun.w.W Yunlong.ɕ.S Qiliqiao.n.N Lanping.n.N Qiliqiao.j.J Yunlong.j.J Eryuan.j.J Jianchuan.n.N Lanping.ʐ.S Heqi.z.S Luobenzhuo.s.S Lanping.ɕ.S Yunlong.s.S Luobenzhuo.ʐ.S Heqi.ɕ.S Qiliqiao.ʃ.S Xiangyun.s.S Eryuan.s.S Qiliqiao.s.S Heqi.sʰ.S Jianchuan.s.S Lanping.s.S Luobenzhuo.ɕ.S Luobenzhuo.k.K Lanping.k.K Yunlong.ɡ.K Luobenzhuo.ʂ.S Qiliqiao.z.S Luobenzhuo.ɡ.K Eryuan.k.K Eryuan.ʂ.S Xiangyun.ɕ.S Jianchuan.ɕ.S Eryuan.ɕ.S Eryuan.ʐ.S Yunlong.ʃ.S Heqi.s.S Yunlong.z.S Qiliqiao.ɕ.S Heqi.ɕʰ.S Luobenzhuo.w.W Jianchuan.v.W Eryuan.w.W Xiangyun.j.J Lanping.w.W Xiangyun.ŋ.N Eryuan.v.W Yunlong.v.W Heqi.v.W Luobenzhuo.v.W Lanping.v.W Luobenzhuo.ŋ.N Qiliqiao.v.W Yunlong.ɥ.J Qiliqiao.w.W Eryuan.ɲ.N Heqi.ɲ.N Xiangyun.n.N Luobenzhuo.ɲ.N Yunlong.w.W Jianchuan.j.J Jianchuan.w.W Luobenzhuo.j.J Heqi.w.W Lanping.j.J Luobenzhuo.m.M Eryuan.ŋ.N Yunlong.n.N Luobenzhuo.n.N Qiliqiao.ɣ.K Heqi.n.N Yunlong.ɲ.N Luobenzhuo.ʔ.H Qiliqiao.m.M Yunlong.ɣ.K Luobenzhuo.ɣ.K Yunlong.ʔ.H Lanping.ɣ.K Luobenzhuo.ɴ.N Lanping.ŋ.N Heqi.ŋ.N Heqi.ɣ.K Yunlong.ʁ.R Jianchuan.ɣ.K Yunlong.ŋ.N Qiliqiao.ŋ.N Jianchuan.ŋ.N Xiangyun.ɣ.K Lanping.m.M Yunlong.f.P Jianchuan.m.M Eryuan.n.N Xiangyun.f.P Xiangyun.m.M Eryuan.m.M Heqi.m.M Heqi.ɔ̃.V Xiangyun.ɔ̃.V Lanping.o.V Luobenzhuo.ʊ.V Heqi.o.V Jianchuan.o.V Jianchuan.õ.V Lanping.ɔ̃.V Xiangyun.ỹ.V Yunlong.ɔ.V Heqi.ũ.V Lanping.õ.V Heqi.ɔ.V Luobenzhuo.ɤ̃.V Luobenzhuo.ɤ.V Xiangyun.ɤ̃.V Jianchuan.ũ.V Jianchuan.u.V Eryuan.o.V Yunlong.ɤ.V Lanping.ɔ.V Heqi.u.V Xiangyun.ũ.V Luobenzhuo.a.V Lanping.u.V Eryuan.ɔ.V Yunlong.a.V Yunlong.ɯ.V Qiliqiao.ɔ.V Luobenzhuo.õ.V Eryuan.u.V Qiliqiao.u.V Xiangyun.ɛ̃.V Yunlong.ɿ.V Xiangyun.a.V Luobenzhuo.ɔ.V Xiangyun.o.V Heqi.ã.V Yunlong.u.V Qiliqiao.ɯ.V Lanping.ã.V Lanping.ɯ ̃ .V Xiangyun.ã.V Heqi.õ.V Xiangyun.u.V Lanping.ɤ.V Luobenzhuo.ʊ̃.V Eryuan.ɤ.V Yunlong.o.V Jianchuan.ɤ̃.V Qiliqiao.ɤ.V Eryuan.²¹.1 Heqi.⁵⁵.1 Qiliqiao.²¹.1 Eryuan.⁴⁴.1 Eryuan.⁵⁵.1 Luobenzhuo.³⁵.1 Heqi.²¹.1 Jianchuan.²¹.1 Xiangyun.⁵⁵.1 Eryuan.l.R Xiangyun.l.R Jianchuan.l.R Xiangyun.x.K Yunlong.l.R Qiliqiao.l.R Luobenzhuo.f.P Lanping.l.R Jianchuan.³³.1 Lanping.⁵⁵.1 Heqi.⁴⁴.1 Qiliqiao.⁴⁴.1 Qiliqiao.³³.1 Eryuan.³³.1 Xiangyun.³³.1 Luobenzhuo.χ.K Eryuan.x.K Eryuan.ɣ.K Qiliqiao.x.K Heqi.x.K Heqi.xʰ.K Lanping.x.K Jianchuan.x.K Luobenzhuo.⁴⁴.1 Yunlong.³¹.1 Qiliqiao.³⁵.1 Luobenzhuo.²¹.1 Jianchuan.³¹.1 Lanping.²¹.1 Heqi.³³.1 Jianchuan.³⁵.1 Jianchuan.⁴⁴.1 Yunlong.⁴⁴.1 Lanping.³¹.1 Heqi.⁴².1 Eryuan.⁴².1 Eryuan.³¹.1 Lanping.³³.1 Xiangyun.²¹.1 Xiangyun.³¹.1 Lanping.⁴².1 Xiangyun.⁴⁴.1 Yunlong.²¹.1 Jianchuan.⁵⁵.1 Luobenzhuo.³¹.1 Xiangyun.³⁵.1 Luobenzhuo.³³.1 Yunlong.³³.1 Lanping.⁴⁴.1 Luobenzhuo.⁵⁵.1 Yunlong.⁵⁵.1 Luobenzhuo.⁴².1 Jianchuan.⁴².1 Yunlong.⁴².1 Xiangyun.⁴².1 Qiliqiao.³¹.1 Qiliqiao.⁴².1 Heqi.³¹.1 Heqi.l.R Luobenzhuo.x.K Qiliqiao.f.P Luobenzhuo.l.R Yunlong.x.K Yunlong.³⁵.1 Qiliqiao.⁵⁵.1 Heqi.³⁵.1 Eryuan.³⁵.1 Lanping.³⁵.1 Luobenzhuo.i.V Jianchuan.i.V Yunlong.i.V Luobenzhuo.ɛ̃.V Lanping.i.V Heqi.ɤ̃.V Jianchuan.ã.V Jianchuan.ɛ.V Heqi.ɿ.V Lanping.ɛ.V Luobenzhuo.ã.V Heqi.ɑ.V Jianchuan.ɿ.V Jianchuan.ɯ.V Jianchuan.a.V Heqi.a.V Luobenzhuo.ʅ.V Heqi.ɛ.V Luobenzhuo.ĩ.V Luobenzhuo.ɯ ̃ .V Lanping.æ.V Xiangyun.ẽ.V Qiliqiao.ɛ̃.V Heqi.ɛ̃.V Qiliqiao.ɛ.V Luobenzhuo.e.V Xiangyun.ɤ.V Luobenzhuo.ɯ.V Xiangyun.ɔ.V Xiangyun.ɯ.V Lanping.ɯ.V Eryuan.ɯ.V Eryuan.ɿ.V Qiliqiao.ɿ.V Qiliqiao.a.V Xiangyun.ɿ.V Lanping.ɿ.V Eryuan.a.V Luobenzhuo.æ ̃ .V Lanping.ɛ̃.V Heqi.ɯ ̃ .V Luobenzhuo.æ.V Xiangyun.e.V Qiliqiao.e.V Lanping.ẽ.V Eryuan.i.V Eryuan.ɛ.V Yunlong.e.V Jianchuan.ɯ ̃ .V Yunlong.ɛ.V Jianchuan.ẽ.V Heqi.e.V Luobenzhuo.ɿ.V Jianchuan.ɛ̃.V Heqi.ɤ.V Xiangyun.ɛ.V Xiangyun.i.V Luobenzhuo.o.V Heqi.ɯ.V Luobenzhuo.ɔ̃.V Luobenzhuo.ɛ.V Lanping.a.V Qiliqiao.i.V Eryuan.pʰ.P Lanping.pʰ.P Qiliqiao.tʰ.T Xiangyun.pʰ.P Yunlong.pʰ.P Qiliqiao.pʰ.P Lanping.t.T Eryuan.tʂʰ.T Jianchuan.t.T Xiangyun.t.T Heqi.t.T Heqi.p.P Qiliqiao.t.T Eryuan.t.T Luobenzhuo.ts.T Luobenzhuo.dʑ.T Luobenzhuo.p.P Lanping.f.P Jianchuan.f.P Yunlong.m.M Yunlong.t.T Yunlong.p.P Eryuan.f.P Heqi.f.P Luobenzhuo.q.K Lanping.tsʰ.T Luobenzhuo.qʰ.K Luobenzhuo.d.T Luobenzhuo.ɖ.T Qiliqiao.ts.T Yunlong.d.T Xiangyun.tʰ.T Yunlong.tʰ.T Lanping.tʰ.T Jianchuan.pʰ.P Eryuan.tʰ.T Jianchuan.tʰ.T Heqi.pʰ.P Luobenzhuo.b.P Jianchuan.p.P Qiliqiao.p.P Eryuan.p.P Luobenzhuo.tʃ.T Luobenzhuo.pʰ.P Lanping.p.P Xiangyun.p.P Yunlong.b.P Heqi.tʰ.T Luobenzhuo.tʃʰ.T Luobenzhuo.tʰ.T Luobenzhuo.t.T Luobenzhuo.ʈ.T Luobenzhuo.tɕ.T Qiliqiao.tsʰ.T Luobenzhuo.cʰ.K Luobenzhuo.ỹ.V Luobenzhuo.ẽ.V Jianchuan.ĩ.V Heqi.∼.N Lanping.∼.N Lanping.ỹ.V Heqi.y.V Lanping.y.V Heqi.ỹ.V Jianchuan.ỹ.V Jianchuan.y.V Xiangyun.y.V Luobenzhuo.y.V Xiangyun.∼.N Jianchuan.ɤ.V Xiangyun.ɯ ̃ .V Luobenzhuo.∼.N Luobenzhuo.u.V Qiliqiao.o.V Heqi.i.V Lanping.ũ.V Heqi.ẽ.V Heqi.ĩ.V Lanping.ĩ.V Jianchuan.e.V Eryuan.e.V Lanping.e.V Qiliqiao.∼.N Xiangyun.ĩ.V Luobenzhuo.ɴ̩.N tones vowels consonants Jianchuan.∼.N Qiliqiao.y.V Luobenzhuo.ũ.V Eryuan.y.V Yunlong.y.V 40 / 52
  67. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Investigation

    v v w v v v ɥ v w w w w v w w e e e ẽ e ẽ ɛ ɛ̃ ẽ æ ̃ æ e ẽ ẽ e e ts ts ts dʑ ts tʂ ɖ ʈ ts ts c tɕ ts dʐ z tʂ ts tʃ dz ɔ ɔ o ɔ o ɔ o õ ɔ̃ ɔ ɔ ɔ̃ o ³⁵ ⁵⁵ ⁵⁵ ⁵⁵ ⁵⁵ ³⁵ ⁵⁵ õ o ɔ ɤ̃ o õ ³⁵ ⁵⁵ ⁵⁵ ³⁵ ³⁵ ³⁵ ³⁵ ³⁵ t t t d t t t d t t ĩ ɛ ɛ̃ i i ɛ̃ i ɛ ɛ ɛ ɛ̃ ɿ ɿ ɿ ɿ ɿ ɿ ʅ ɿ ɿ ɤ ʊ ɤ ɤ ɤ̃ ɤ ɤ ɤ ⁵⁵ ʁ ɣ ɣ ɣ ɣ ɣ ʔ ɣ ɣ æ ɛ ɛ ɛ ɛ̃ w ɛ̃ ã ʊ̃ ɤ̃ ɔ̃ õ ɤ ɤ̃ o ɯ ɯ ɯ ̃ ɯ ɯ ̃ ɯ ̃ ɯ ɯ ̃ ɯ u o u ũ ũ i i ĩ i a a e a a ã ã ỹ ĩ ĩ ĩ i i cʰ tʃʰ tsʰ ʈʰ tʂʰ tsʰ tsʰ tsʰ ɯ ỹ ɯ ɯ ̃ ɑ ɯ u u ɤ u u u u ũ tsʰ tʂʰ tsʰ tʃʰ tsʰ tsʰ a a ã ã a ɔ̃ ŋ ŋ ŋ ŋ ŋ ŋ ŋ ɴ f f f f f f f f ²¹ ²¹ ²¹ ²¹ ²¹ ²¹ ³¹ ²¹ ɕ ɕ ɕ ɕ ɕ ɕ ɕ ɕʰ ³³ ³³ ³³ ³³ ³³ ³³ ³³ tɕʰ tɕʰ tɕʰ tɕʰ tɕʰ tɕʰ tɕʰ tɕʰ tɕ tɕ dʑ tɕ tɕ tɕ tɕ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ n n ɲ n n ɲ j n n ʔ ɲ n n ɲ ɲ ʂ z s s s s ɕ s ʃ s ʂ s ʃ sʰ s j ʑ j ɣ j j j j j z z z ʐ ʐ z ʐ kʲ k k k k q k k kʲ k x x kʰ x kʰ qʰ y tɕ y y ³³ p y ²¹ y ũ ỹ ỹ ỹ y y y kʰ kʰ kʰ kʰ b kʰʲ kʰ kʰ p p p ũ p p p p tʃ b pʰ χ xʰ a x x x x x ɡ ŋ ɡ k m m m m m m m m ɴ̩ ³¹ ³¹ ³¹ ∼ ³¹ ³¹ ³¹ ³¹ tʰ tʰ tʰ tʰ pʰ tʰ tʰ tʰ tʰ pʰ pʰ ∼ pʰ ∼ pʰ ∼ pʰ ∼ ∼ pʰ l l l l l ⁴² ⁴² ⁴² l ⁴² l ⁴² ⁴² ⁴² ⁴² l 40 / 52
  68. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Investigation

    v v w v v v ɥ v w w w w v w w e e e ẽ e ẽ ɛ ɛ̃ ẽ æ ̃ æ e ẽ ẽ e e ts ts ts dʑ ts tʂ ɖ ʈ ts ts c tɕ ts dʐ z tʂ ts tʃ dz ɔ ɔ o ɔ o ɔ o õ ɔ̃ ɔ ɔ ɔ̃ o ³⁵ ⁵⁵ ⁵⁵ ⁵⁵ ⁵⁵ ³⁵ ⁵⁵ õ o ɔ ɤ̃ o õ ³⁵ ⁵⁵ ⁵⁵ ³⁵ ³⁵ ³⁵ ³⁵ ³⁵ t t t d t t t d t t ĩ ɛ ɛ̃ i i ɛ̃ i ɛ ɛ ɛ ɛ̃ ɿ ɿ ɿ ɿ ɿ ɿ ʅ ɿ ɿ ɤ ʊ ɤ ɤ ɤ̃ ɤ ɤ ɤ ⁵⁵ ʁ ɣ ɣ ɣ ɣ ɣ ʔ ɣ ɣ æ ɛ ɛ ɛ ɛ̃ w ɛ̃ ã ʊ̃ ɤ̃ ɔ̃ õ ɤ ɤ̃ o ɯ ɯ ɯ ̃ ɯ ɯ ̃ ɯ ̃ ɯ ɯ ̃ ɯ u o u ũ ũ i i ĩ i a a e a a ã ã ỹ ĩ ĩ ĩ i i cʰ tʃʰ tsʰ ʈʰ tʂʰ tsʰ tsʰ tsʰ ɯ ỹ ɯ ɯ ̃ ɑ ɯ u u ɤ u u u u ũ tsʰ tʂʰ tsʰ tʃʰ tsʰ tsʰ a a ã ã a ɔ̃ ŋ ŋ ŋ ŋ ŋ ŋ ŋ ɴ f f f f f f f f ²¹ ²¹ ²¹ ²¹ ²¹ ²¹ ³¹ ²¹ ɕ ɕ ɕ ɕ ɕ ɕ ɕ ɕʰ ³³ ³³ ³³ ³³ ³³ ³³ ³³ tɕʰ tɕʰ tɕʰ tɕʰ tɕʰ tɕʰ tɕʰ tɕʰ tɕ tɕ dʑ tɕ tɕ tɕ tɕ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ n n ɲ n n ɲ j n n ʔ ɲ n n ɲ ɲ ʂ z s s s s ɕ s ʃ s ʂ s ʃ sʰ s j ʑ j ɣ j j j j j z z z ʐ ʐ z ʐ kʲ k k k k q k k kʲ k x x kʰ x kʰ qʰ y tɕ y y ³³ p y ²¹ y ũ ỹ ỹ ỹ y y y kʰ kʰ kʰ kʰ b kʰʲ kʰ kʰ p p p ũ p p p p tʃ b pʰ χ xʰ a x x x x x ɡ ŋ ɡ k m m m m m m m m ɴ̩ ³¹ ³¹ ³¹ ∼ ³¹ ³¹ ³¹ ³¹ tʰ tʰ tʰ tʰ pʰ tʰ tʰ tʰ tʰ pʰ pʰ ∼ pʰ ∼ pʰ ∼ pʰ ∼ ∼ pʰ l l l l l ⁴² ⁴² ⁴² l ⁴² l ⁴² ⁴² ⁴² ⁴² l 40 / 52
  69. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Investigation

    n n n n n j j j j j ʑ j ɣ j j ɲ n n n ɲ ɲ ɲ ɲ ʔ 40 / 52
  70. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Investigation

    ²¹ ²¹ ²¹ ²¹ ²¹ ²¹ ²¹ ³¹ ²¹ ³⁵ ⁵⁵ ⁵⁵ ³⁵ ⁵⁵ ⁵⁵ ⁵⁵ ⁵⁵ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ³³ ³³ ³³ ³³ ³³ ³³ ³³ ³³ ³¹ ³¹ ³¹ ³¹ ³¹ ³¹ ³¹ ⁴² ⁴² ⁴² ⁴² ⁴² ⁴² ⁴² ⁴² ⁵⁵ ³⁵ ³⁵ ³⁵ ³⁵ ⁵⁵ ³⁵ ³⁵ 40 / 52
  71. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Investigation

    ²¹ ²¹ ²¹ ²¹ ²¹ ²¹ ²¹ ³¹ ²¹ ³⁵ ⁵⁵ ⁵⁵ ³⁵ ⁵⁵ ⁵⁵ ⁵⁵ ⁵⁵ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ⁴⁴ ³³ ³³ ³³ ³³ ³³ ³³ ³³ ³³ ³¹ ³¹ ³¹ ³¹ ³¹ ³¹ ³¹ ⁴² ⁴² ⁴² ⁴² ⁴² ⁴² ⁴² ⁴² ⁵⁵ ³⁵ ³⁵ ³⁵ ³⁵ ⁵⁵ ³⁵ ³⁵ 44 35/55 31 42 33 21 40 / 52
  72. Non-Tree-Like Processes in Language Evolution Sound Change Sound Change: Chances/Challenges

    so far, we only explore, we do not yet analyse the patterns as a next step, we need to start thinking about ways to infer potential directions of changes we also need to find more rigorous ways to handle the context of change patterns, as context is one of the major factors conditioning sound change we only use monopartite networks in this exploration, and do not really illustrate which sound occurs in which language for a deep analysis, we will need to include the languages in which the sounds occur into our analysis need to include the information 41 / 52
  73. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Background

    hand arm foot day m eat animal day sand moon leg T₁ 42 / 52
  74. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Background

    hand arm foot day m eat animal day sand moon leg T₁ hand arm foot day m eat animal day sand moon leg T₂ 42 / 52
  75. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Background

    hand arm foot day m eat animal day sand moon leg T₂ ? ? ? 42 / 52
  76. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Background

    hand arm foot day m eat animal day sand moon leg T₂ hand arm foot day m eat animal sun sand moon leg 42 / 52
  77. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Characteristics

    Semantic change plays a crucial role in language change. Although most linguists assume that it proceeds according to certain general patterns, we currently lack the empirical basis to pursue the question in depth. Normally, semantic change proceeds by cumulation and reduction. 43 / 52
  78. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Characteristics

    German “head” Kopf . k ɔ p͡f Pre-German “head” *kop – k ɔ p “vessel” Proto- Germanic *kuppa- k u pː a “vessel” POLYSEMY PHASE FORM MEANING MONOSEMY PHASE MONOSEMY PHASE CUMULATION REDUCTION 43 / 52
  79. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Characteristics

    “cup” CONTEST TROPHY [kʌp] CUP English polysemy structure for cup 43 / 52
  80. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Characteristics

    “head, cup” CUP HEAD [kɔp] TOP Dutch polysemy structure for kop 43 / 52
  81. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Characteristics

    “head” HEAD TOP [kɔp͡f] CHIEF German polysemy structure for Kopf 43 / 52
  82. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Investigation

    Key Concept Russian German ... 1.1 world mir, svet Welt ... 1.21 earth, land zemlja Erde, Land ... 1.212 ground, soil počva Erde, Boden ... 1.420 tree derevo Baum ... 1.430 wood derevo Wald ... ... ... ... ... ... 44 / 52
  83. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Investigation

    CLICS: Crosslinguistic Colexifications - 221 Languages - 64 language families - 1280 concepts - 301,498 words - 45,667 polysemies (colexifications) - 16,239 different links between concepts - http://clics.lingpy.org 44 / 52
  84. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Investigation

    684 678 871 1043 6 30 129 196 1243 128 869 853 650 344 1103 150 185 627 232 709 1035 1206 177 97 311 496 606 137 207 444 840 1077 325 222 1063 1138 1204 1258 559 723 495 766 914 38 1101 652 865 891 872 633 291 980 700 144 410 430 1025 406 464 787 622 131 242 918 275 1159 99 1174 671 1038 786 705 641 760 1259 356 391 197 10 214 299 63 191 619 644 792 1205 897 67 1231 213 226 747 681 399 841 439 773 123 800 16 1067 1227 696 417 550 68 76 108 360 1244 339 500 81 867 79 1097 98 96 833 771 715 455 380 1268 1186 1046 39 252 1228 66 23 1112 133 676 336 739 1150 1071 986 485 112 372 1109 830 721 1053 1057 601 573 556 527 1248 614 488 908 499 1002 309 442 814 1193 569 458 258 563 653 682 774 70 1151 948 801 1082 243 47 71 83 153 1265 934 85 1215 1199 523 581 422 21 358 1261 111 354 219 759 15 890 261 1222 141 158 74 806 1031 845 770 850 903 1224 419 754 433 798 188 1256 613 528 208 539 323 981 132 1055 1001 790 804 844 1118 907 640 446 815 923 498 201 1184 578 566 427 532 452 151 750 598 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61 240 1263 199 648 832 289 522 368 1091 931 982 949 400 119 388 811 53 59 1069 708 952 545 763 1238 184 825 377 1242 1233 262 635 269 1062 1061 1073 933 17 1247 352 64 384 50 632 736 1246 822 781 758 1 939 595 778 105 860 1049 1066 1072 995 503 370 919 1149 1127 1128 972 1126 245 921 973 675 587 1235 960 928 926 1143 548 1250 86 1021 32 1068 719 965 259 1070 863 638 303 324 873 249 892 976 1007 722 36 459 293 165 209 557 1245 788 862 651 900 31 483 236 935 1052 115 294 680 831 44 453 206 971 1273 170 753 256 1148 200 450 382 1240 561 615 317 572 725 870 438 139 1011 646 1117 392 45 276 264 704 1080 174 1050 808 1197 508 576 225 562 471 1217 333 1014 593 92 1034 611 1171 312 802 1253 29 902 244 582 466 668 878 341 432 1163 625 904 164 467 1195 1232 796 828 281 629 349 1166 411 369 387 1208 394 415 1000 58 1098 148 287 1223 818 263 220 838 876 313 260 65 1165 5 355 106 1172 490 718 171 1139 163 785 881 887 1169 319 585 553 894 306 314 1041 1009 799 674 848 1201 1004 689 1085 1218 1145 1170 228 911 279 73 104 690 1254 402 340 169 693 868 893 1018 78 1092 194 555 198 834 1249 997 932 237 1176 666 956 624 1262 541 520 795 866 702 4 734 1095 1180 728 964 1079 271 842 1241 1056 154 751 353 905 1136 504 909 910 1133 362 583 670 1124 381 1216 215 178 571 470 142 376 1154 172 296 533 364 963 152 797 1213 803 1051 738 426 1036 1153 637 823 915 428 1075 560 547 1137 35 882 89 511 1122 805 494 1130 1188 1086 1236 669 588 930 703 942 18 655 335 155 710 1156 1028 465 147 183 414 1221 273 166 1054 278 55 460 812 1090 810 180 768 143 156 404 367 1182 231 288 136 456 82 529 970 1016 729 395 187 604 408 330 1064 34 1267 847 726 543 677 642 940 645 958 683 695 864 1058 605 1084 451 443 699 1167 959 925 1198 227 886 628 1178 337 991 813 657 1185 1039 769 1081 484 712 1189 944 1207 322 33 685 424 80 270 937 1177 283 1237 816 130 161 189 77 300 1026 463 1104 326 589 60 983 474 1093 744 748 554 292 41 267 984 373 1214 957 1024 969 507 37 874 1030 630 579 962 535 706 688 122 497 1060 1083 1027 102 510 405 1134 658 617 936 929 363 1175 361 536 534 1219 181 386 884 418 558 8 479 979 551 505 316 298 26 315 761 202 1144 176 473 348 134 639 663 717 885 924 149 49 1078 1040 57 167 764 1173 673 280 1152 277 1272 1065 272 827 531 607 1123 257 996 436 9 826 234 1096 875 525 304 1108 475 1132 714 846 540 716 1005 1105 357 1162 694 920 743 28 994 1200 168 1266 420 515 568 755 895 218 916 730 807 210 375 854 1010 879 1125 268 1129 1114 1255 1158 1279 487 486 398 597 661 135 565 621 193 321 1230 513 654 265 612 737 855 211 1196 246 1264 584 338 749 1271 434 121 423 509 839 1147 656 230 239 489 14 469 22 1044 351 448 282 329 961 254 989 371 284 223 843 821 24 1023 643 819 285 514 746 757 791 138 186 849 93 951 127 877 1088 518 1164 1260 501 54 190 95 43 205 1276 116 146 662 217 461 883 204 1033 310 472 12 412 332 817 649 794 1037 943 927 481 968 425 109 195 857 1121 564 687 664 724 87 1120 88 449 429 255 987 992 1111 591 575 491 720 851 328 941 990 1019 993 1087 955 580 1226 975 1099 732 235 779 365 1234 441 609 247 334 91 1251 1131 913 691 52 274 1017 435 90 407 480 1239 13 623 0 266 626 295 954 1059 552 898 858 772 526 1115 48 1161 125 590 454 1020 1141 203 740 1146 342 820 1220 56 320 416 27 401 476 19 120 1203 445 789 775 888 567 378 1076 160 162 409 731 631 374 538 837 44 / 52
  85. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Investigation

    Concept "money" is part of a cluster with the central concept "fishscale" with a total of 10 nodes. Hover over forms for each link. Click on the forms to check their sources. Click HERE to export the current network. ty: Line weights: Coloring: Family silver leather fishscale bark coin fur snail skin, hide money shell 49 links for "silver" and "money": Language Family Form 1. Ignaciano Arawakan ne 2. Aymara, Central Aymaran ḳulʸḳi 3. Tsafiki Barbacoan kaˈla 4. Seselwa Creole French Creole larzan 5. Miao, White Hmong-Mien nyiaj 6. Breton Indo-European arhant 7. French Indo-European argent 8. Gaelic, Irish Indo-European airgead 9. Welsh Indo-European arian 10. Cofán Isolate koriΦĩʔdi 44 / 52
  86. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Investigation

    Concept "wheel" is part of a cluster with the central concept "leg" with a total of 11 nodes. Hover over the e each link. Click on the forms to check their sources. Click HERE to export the current network. ity: Line weights: Coloring: Geolocation sphere, ball round footprint foot calf of leg circle thigh wheel leg hip buttocks 6 links for "foot" and "wheel": Language Family Form 1. Cofán Isolate c̷ɨʔtʰe 2. Puinave Isolate sim 3. Yaminahua Panoan taɨ 4. Wayampi Tupi pɨ 5. Pumé Unclassified taɔ 6. Ninam Yanomam mãhuk 44 / 52
  87. Non-Tree-Like Processes in Language Evolution Semantic Change Semantic Change: Chances/Challenges

    so far, we use monopartite networks for our modeling and rather simple community-detection algorithms, as a result, we loose signal, since words do not change their meaning in isolation, but we know that semantic change is often interconnected: the change of the meaning in one word goes along with changes in other words bipartite networks seem to be a straightforward way to model our networks to account for interdependencies we only compare the meanings of words in isolation, but we know that the meaning of a word can be compositional, involving complex structures of denotation (compare “apple tree”, “grandfather”, etc.) by investigating partial colexifications (partial polysemy) we may gain new insights into the roads of perception and denotation 45 / 52
  88. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Background

    One beer please! A beer for me! Beer? Please? You have beer? I'm thirsty, but I do not drink water, can you help me? I want the same as everybody else here. 46 / 52
  89. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Background

    Tokens Units Relations Levels sounds phonemes phonotactics phonemics words morphemes morpho-tactics morphemics sentences constructions grammatical syntax 47 / 52
  90. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Background

    English steam train German Dampfzug (steam + train) Chinese huǒ chē (fire + vehicle) Russian parovoz (steam + driver) French locomotive à vapeur (locomotive + with + steam) 47 / 52
  91. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Background

    We can think of many different ways of how to express a certain meaning, but although the potential is virtually un- limited, the roads of denotation, that is, the mechanisms by which words are formed from morphemes, follow certain re- curring patterns across all languages. Comparing these pat- terns can give us important insights into human cognition. 47 / 52
  92. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Background

    On the other hand, the fact that words are often formed from smaller parts, be it by compounding existing words, or us- ing specific morphemes to derive new words, makes it very difficult to identify homologous words automatically! What are the mechanisms by which the roads of denotation are created across the worlds languages? How can we distinguish direct homologues (orthologues) from indirect ones (partial homologues, etc.) in phylogenetic models or homologue detection? 47 / 52
  93. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Background

    'soh₂-wl̩- sh₂uˈen- SUN Indo-European soːwel- sunːoː- SUN Germanic 48 / 52
  94. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Background

    'soh₂-wl̩- sh₂uˈen- SUN Indo-European soːwel- sunːoː- SUN Germanic zɔnə SUN German suːl SUN Swedish 48 / 52
  95. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Background

    'soh₂-wl̩- sh₂uˈen- SUN Indo-European soːwel- sunːoː- SUN Germanic soːl- SUN Romance zɔnə SUN German suːl SUN Swedish 48 / 52
  96. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Background

    'soh₂-wl̩- sh₂uˈen- SUN Indo-European soːwel- sunːoː- SUN Germanic soːl- SUN soːlikul- SMALL SUN Romance zɔnə SUN German suːl SUN Swedish 48 / 52
  97. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Background

    'soh₂-wl̩- sh₂uˈen- SUN Indo-European soːwel- sunːoː- SUN Germanic soːl- SUN soːlikul- SMALL SUN Romance solej SUN French sol SUN Spanish zɔnə SUN German suːl SUN Swedish 48 / 52
  98. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Background

    'soh₂-wl◌̩ - sh₂uˈen- SUN Indo-European soːwel- sunːoː- SUN Germanic soːl- SUN soːlikul- SMALL SUN Romance solej SUN French sol SUN Spanish zɔnə SUN German suːl SUN Swedish SEM ANTIC SHIFT M O RPH O LO G ICAL CH AN G E M O R PH O LO G ICA L CH A N G E MORPHOLOGICAL CHANGE MORPHOLOGICAL CHANGE 48 / 52
  99. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Background

    Italian dare French donner Indo-European *deh₃- *deh₃-no- Latin dare dōnum dōnāre Italian sole French soleil Swedish sol German Sonne Germanic *sōwel- *sunnō- Latin sol soliculus Indo-European *sóh₂-wl̩ - *sh₂én- A B 48 / 52
  100. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Investigation

    Automatic Detection of Partial Cognates: The Problem languages in which words are frequently created by compounding the identification of homologous words is extremely difficult current phylogenetic models cannot handle partial homology, and as a result, very important signal is lost current methods for automatic homologue detection in linguistics also cannot handle partial homologues and show a very low accuracy in languages where compounding is frequent (especially in the languages of South-East Asia) 49 / 52
  101. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Investigation

    German m oː n t - English m uː n - - Danish m ɔː n - ə Swedish m oː n - e 49 / 52
  102. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Investigation

    German m oː n t - English m uː n - - Danish m ɔː n - ə Swedish m oː n - e Fúzhōu ŋ u o ʔ ⁵ - - - - - - - - - - Měixiàn ŋ i a t ⁵ - - - - - k u o ŋ ⁴⁴ Guǎngzhōu j - y t ² l - œ ŋ ²² - - - - - Běijīng - y ɛ - ⁵¹ l i ɑ ŋ - - - - - - 49 / 52
  103. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Investigation

    German m oː n t - English m uː n - - Danish m ɔː n - ə Swedish m oː n - e Fúzhōu ŋ u o ʔ ⁵ - - - - - - - - - - Měixiàn ŋ i a t ⁵ - - - - - k u o ŋ ⁴⁴ Guǎngzhōu j - y t ² l - œ ŋ ²² - - - - - Běijīng - y ɛ - ⁵¹ l i ɑ ŋ - - - - - - "MOON" "MOON" "SHINE" "LIGHT" 49 / 52
  104. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Investigation

    Automatic Detection of Partial Cognates: The Solution use sequence similarity networks to determine the similarity between the parts of the words in the data apply filters to reduce the edges in the similarity networks use a community detection algorithm to further partition the data into clusters 49 / 52
  105. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Investigation

    Fúzhōu ŋuoʔ⁵ Měixiàn ŋiat⁵ 0.44 kuoŋ⁴⁴ 0.78 0.78 Wēnzhōu y²¹ ȵ 0.30 0.35 0.67 ku ³ ɔ ⁵ 0.80 0.85 0.27 0.67 vai¹³ 0.85 0.85 0.82 0.73 0.73 Běijīng y ¹ ɛ⁵ 0.77 0.84 0.73 0.56 0.56 0.66 li ŋ¹ ɑ 0.78 0.78 0.44 0.67 0.82 0.82 0.80 ŋiat⁵ kuoŋ⁴⁴ ŋuoʔ⁵ ȵy²¹ yɛ⁵¹ kuɔ³⁵ liɑŋ¹ vai¹³ ŋiat⁵ vai¹³ kuoŋ⁴⁴ ŋuoʔ⁵ liɑŋ¹ yɛ⁵¹ ȵy²¹ kuɔ³⁵ ȵy²¹ kuɔ³⁵ ŋiat⁵ yɛ⁵¹ liɑŋ¹ ŋuoʔ⁵ kuoŋ⁴⁴ vai¹³ B C D A 49 / 52
  106. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Investigation

    Fúzhōu ŋuoʔ⁵ Měixiàn ŋiat⁵ 0.44 kuoŋ⁴⁴ 0.78 0.78 Wēnzhōu y²¹ ȵ 0.30 0.35 0.67 ku ³ ɔ ⁵ 0.80 0.85 0.27 0.67 vai¹³ 0.85 0.85 0.82 0.73 0.73 Běijīng y ¹ ɛ⁵ 0.77 0.84 0.73 0.56 0.56 0.66 li ŋ¹ ɑ 0.78 0.78 0.44 0.67 0.82 0.82 0.80 ŋiat⁵ kuoŋ⁴⁴ ŋuoʔ⁵ ȵy²¹ yɛ⁵¹ kuɔ³⁵ liɑŋ¹ vai¹³ ŋiat⁵ vai¹³ kuoŋ⁴⁴ ŋuoʔ⁵ liɑŋ¹ yɛ⁵¹ ȵy²¹ kuɔ³⁵ ȵy²¹ kuɔ³⁵ ŋiat⁵ yɛ⁵¹ liɑŋ¹ ŋuoʔ⁵ kuoŋ⁴⁴ vai¹³ B C D A 49 / 52
  107. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Investigation

    Fúzhōu ŋuoʔ⁵ Měixiàn ŋiat⁵ 0.44 kuoŋ⁴⁴ 0.78 0.78 Wēnzhōu y²¹ ȵ 0.30 0.35 0.67 ku ³ ɔ ⁵ 0.80 0.85 0.27 0.67 vai¹³ 0.85 0.85 0.82 0.73 0.73 Běijīng y ¹ ɛ⁵ 0.77 0.84 0.73 0.56 0.56 0.66 li ŋ¹ ɑ 0.78 0.78 0.44 0.67 0.82 0.82 0.80 ŋiat⁵ kuoŋ⁴⁴ ŋuoʔ⁵ ȵy²¹ yɛ⁵¹ kuɔ³⁵ liɑŋ¹ vai¹³ ŋiat⁵ vai¹³ kuoŋ⁴⁴ ŋuoʔ⁵ liɑŋ¹ yɛ⁵¹ ȵy²¹ kuɔ³⁵ ȵy²¹ kuɔ³⁵ ŋiat⁵ yɛ⁵¹ liɑŋ¹ ŋuoʔ⁵ kuoŋ⁴⁴ vai¹³ B C D A 49 / 52
  108. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Investigation

    Fúzhōu ŋuoʔ⁵ Měixiàn ŋiat⁵ 0.44 kuoŋ⁴⁴ 0.78 0.78 Wēnzhōu y²¹ ȵ 0.30 0.35 0.67 ku ³ ɔ ⁵ 0.80 0.85 0.27 0.67 vai¹³ 0.85 0.85 0.82 0.73 0.73 Běijīng y ¹ ɛ⁵ 0.77 0.84 0.73 0.56 0.56 0.66 li ŋ¹ ɑ 0.78 0.78 0.44 0.67 0.82 0.82 0.80 ŋiat⁵ kuoŋ⁴⁴ ŋuoʔ⁵ ȵy²¹ yɛ⁵¹ kuɔ³⁵ liɑŋ¹ vai¹³ ŋiat⁵ vai¹³ kuoŋ⁴⁴ ŋuoʔ⁵ liɑŋ¹ yɛ⁵¹ ȵy²¹ kuɔ³⁵ ȵy²¹ kuɔ³⁵ ŋiat⁵ yɛ⁵¹ liɑŋ¹ ŋuoʔ⁵ kuoŋ⁴⁴ vai¹³ B C D A 49 / 52
  109. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Investigation

    Fúzhōu ŋuoʔ⁵ Měixiàn ŋiat⁵ 0.44 kuoŋ⁴⁴ 0.78 0.78 Wēnzhōu y²¹ ȵ 0.30 0.35 0.67 ku ³ ɔ ⁵ 0.80 0.85 0.27 0.67 vai¹³ 0.85 0.85 0.82 0.73 0.73 Běijīng y ¹ ɛ⁵ 0.77 0.84 0.73 0.56 0.56 0.66 li ŋ¹ ɑ 0.78 0.78 0.44 0.67 0.82 0.82 0.80 ŋiat⁵ kuoŋ⁴⁴ ŋuoʔ⁵ ȵy²¹ yɛ⁵¹ kuɔ³⁵ liɑŋ¹ vai¹³ ŋiat⁵ vai¹³ kuoŋ⁴⁴ ŋuoʔ⁵ liɑŋ¹ yɛ⁵¹ ȵy²¹ kuɔ³⁵ ȵy²¹ kuɔ³⁵ ŋiat⁵ yɛ⁵¹ liɑŋ¹ ŋuoʔ⁵ kuoŋ⁴⁴ vai¹³ B C D A 49 / 52
  110. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Investigation

    Automatic Detection of Partial Cognates: The Solution with help of sequence similarity networks, we (List, Lopez, and Bapteste 2016) have created the first algorithm to detect partial cognates (homologues) in linguistic data our method outperforms traditional methods largely, reaching a plus of more than 5% in accuracy on our test sets the algorithms is also very fast and can be easily applied to considerably large datasets 49 / 52
  111. Non-Tree-Like Processes in Language Evolution Word Formation Word Formation: Chances/Challenges

    with our new algorithm for partial cognate detection with help of sequence similarity networks, we have opened the door for the fast creation of large datasets for language families in historical linguistics which could so far not be sufficiently analysed with phylogenetic methods unfortunately, however, we lack the phylogenetic models to sufficiently further analyse the data (in List 2016, it is shown, that we need multi-state models in order to handle partial homology sufficiently) our knowledge about the underlying processes from an evolutionary perspective is also not very profound, and we need to try to find new ways to study the roads of denotation across the languages in the world 50 / 52
  112. Language evolution is characterized by a large number of non-tree-like

    processes which have triggered the diversity of the linguistic diversity we observe today. By reducing the investigation of language evolution to the search for phylo- genetic trees, we deprive ourselves of an abundance of data which can offer new explanations for the development of in- dividual language families, universal characteristics of lan- guage change, and even universal characteristics of human cognition. Whether evolutionary processes in biology and linguistics are indeed similar is difficult to tell. However, when carefully comparing the commonalities, we may find ways to success- fully transfer and adapt methods across disciplines, but also to gain new insights into overarching processes of evolution. 52 / 52