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Modelling Chinese dialect evolution

Modelling Chinese dialect evolution

Talk held at the workshop Beyond Phylogeny: Quantitative diachronic explanations of language diversity, August 29 - Septemper 1, Stockholm.

Johann-Mattis List

August 30, 2012
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  1. .
    .
    . .
    .
    .
    .
    Modelling Chinese Dialect Evolution
    Johann-Mattis List∗, Shijulal Nelson-Sathi+, and Tal Dagan+
    ∗Institute for Romance Languages and Literature
    +Institute for Genomic Microbiology
    Heinrich Heine University Düsseldorf
    2012/08/31
    1 / 30

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  2. Structure of the Talk
    .
    . .
    1 Languages
    Languages
    Diasystems
    Change
    .
    . .
    2 Modelling Language History
    Trees
    Waves
    Networks
    .
    . .
    3 Modelling Chinese Dialect History
    Data
    Analysis
    Results
    2 / 30

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  3. Languages
    语言
    language
    1
    язык
    språk
    1
    Languages
    3 / 30

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  4. Languages Languages
    Languages and Dialects
    Norwegian, Danish, and Swedish are different languages.
    .
    .
    Beijing-Chinese, Shanghai-Chinese, and Hakka-Chinese
    are dialects of the same Chinese language.
    4 / 30

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  5. Languages Languages
    Languages and Dialects
    Beijing Chinese 1 iou²¹ i⁵⁵ xuei³⁵ pei²¹fəŋ⁵⁵ kən⁵⁵ tʰai⁵¹iaŋ¹¹ t͡ʂəŋ⁵⁵ ʦai⁵³ naɚ⁵¹ t͡ʂəŋ⁵⁵luən⁵¹
    Hakka Chinese 1 iu³³ it⁵⁵ pai³³a¹¹ pet³³fuŋ³³ tʰuŋ¹¹ ɲit¹¹tʰeu¹¹ hɔk³³ e⁵³ au⁵⁵
    Shanghai Chinese 1 ɦi²² tʰɑ̃⁵⁵ ʦɿ²¹ poʔ³foŋ⁴⁴ taʔ⁵ tʰa³³ɦiã⁴⁴ ʦəŋ³³ hɔ⁴⁴ ləʔ¹lə²³ʦa⁵³
    Beijing Chinese 2 ʂei³⁵ də⁵⁵ pən³⁵ liŋ²¹ ta⁵¹
    Hakka Chinese 2 man³³ ɲin¹¹ kʷɔ⁵⁵ vɔi⁵³
    Shanghai 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
    5 / 30

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  6. Languages Languages
    Languages and Dialects
    From the perspective of the lexicon and the sound system,
    the Chinese dialects are at least equally if not more different
    than the Scandinavian languages.
    5 / 30

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  7. Languages Diasystems
    Language as a Diasystem
    Languages are complex aggregates of different linguistic
    systems that ‘coexist and influence each other’ (Coseriu
    1973: 40, my translation).
    .
    .
    6 / 30

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  8. Languages Diasystems
    Language as a Diasystem
    Languages are complex aggregates of different linguistic
    systems that ‘coexist and influence each other’ (Coseriu
    1973: 40, my translation).
    .
    .
    A linguistic diasystem requires a “roof language” (Goossens
    1973:11), i.e. a linguistic variety that serves as a standard
    for interdialectal communication.
    6 / 30

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  9. Languages Diasystems
    Language as a Diasystem
    Standard Language
    Diatopic Varieties
    Diastratic Varieties
    Diaphasic Varieties
    7 / 30

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  10. Languages Change
    Change
    8 / 30

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  11. Languages Change
    Change
    expected Mandarin [ma₅₅po₂₁lou]
    8 / 30

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  12. Languages Change
    Change
    expected Mandarin [ma₅₅po₂₁lou]
    attested Mandarin [wan₅₁paw₂₁lu₅₁]
    8 / 30

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  13. Languages Change
    Change
    expected Mandarin [ma₅₅po₂₁lou]
    attested Mandarin [wan₅₁paw₂₁lu₅₁]
    explanation Cantonese [maːn₂₂pow₃₅low₃₂]
    8 / 30

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  14. Languages Change
    Change
    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
    9 / 30

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  15. Modelling Language History
    Modelling Language History
    10 / 30

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  16. Modelling Language History Trees
    Dendrophilia
    August Schleicher
    (1821-1868)
    11 / 30

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  17. Modelling Language History Trees
    Dendrophilia
    August Schleicher
    (1821-1868)
    These assumptions that logically
    follow from the results of our re-
    search can be best illustrated with
    help of a branching tree. (Schle-
    icher 1853: 787, my translation)
    11 / 30

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  18. Modelling Language History Trees
    Dendrophilia
    Schleicher (1853)
    12 / 30

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  19. Modelling Language History Waves
    Dendrophobia
    Johannes Schmidt
    (1843-1901)
    13 / 30

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  20. Modelling Language History Waves
    Dendrophobia
    Johannes Schmidt
    (1843-1901)
    No matter how we look at it, as long
    as we stick to the assumption that
    today’s languages originated from
    their common proto-language via
    multiple furcation, we will never be
    able to explain all facts in a scientifi-
    cally adequate way. (Schmidt 1872:
    17, my translation)
    13 / 30

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  21. Modelling Language History Waves
    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 / 30

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  22. Modelling Language History Waves
    Dendrophobia
    Schmidt (1875)
    15 / 30

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  23. Modelling Language History Waves
    Dendrophobia
    Meillet (1908)
    Hirt (1905)
    Bloomfield (1933)
    Bonfante (1931)
    16 / 30

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  24. Modelling Language History Networks
    Phylogenetic Networks
    Trees are bad because
    17 / 30

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  25. Modelling Language History Networks
    Phylogenetic Networks
    Trees are bad because
    they are difficult to
    reconstruct............
    17 / 30

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  26. Modelling Language History Networks
    Phylogenetic Networks
    Trees are bad because
    they are difficult to
    reconstruct............
    languages do not separate in
    split processes
    17 / 30

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  27. Modelling Language History Networks
    Phylogenetic Networks
    Trees are bad because
    they are difficult to
    reconstruct............
    languages do not separate in
    split processes
    they are boring, since they
    only capture certain aspects of
    language history, namely the
    vertical relations
    17 / 30

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  28. Modelling Language History Networks
    Phylogenetic Networks
    Trees are bad because
    they are difficult to
    reconstruct............
    languages do not separate in
    split processes
    they are boring, since they
    only capture certain aspects of
    language history, namely the
    vertical relations
    Waves are bad because
    nobody knows how to
    reconstruct them
    17 / 30

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  29. Modelling Language History Networks
    Phylogenetic Networks
    Trees are bad because
    they are difficult to
    reconstruct............
    languages do not separate in
    split processes
    they are boring, since they
    only capture certain aspects of
    language history, namely the
    vertical relations
    Waves are bad because
    nobody knows how to
    reconstruct them
    languages still separate, even
    if not in split processes
    17 / 30

    View Slide

  30. Modelling Language History Networks
    Phylogenetic Networks
    Trees are bad because
    they are difficult to
    reconstruct............
    languages do not separate in
    split processes
    they are boring, since they
    only capture certain aspects of
    language history, namely the
    vertical relations
    Waves are bad because
    nobody knows how to
    reconstruct them
    languages still separate, even
    if not in split processes
    they are boring, since they
    only capture certain aspects of
    language history, namely, the
    horizontal relations
    17 / 30

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  31. Modelling Language History Networks
    Phylogenetic Networks
    Hugo Schuchardt
    (1842-1927)
    18 / 30

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  32. Modelling Language History Networks
    Phylogenetic 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)
    18 / 30

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  33. Modelling Language History Networks
    Phylogenetic Networks
    19 / 30

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  34. Modelling Chinese Dialect History

    1

    1

    1
    ?
    首首 首 首
    Modelling Chinese Dialect History
    20 / 30

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  35. Modelling Chinese Dialect History Data
    Data
    21 / 30

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  36. Modelling Chinese Dialect History Data
    Data
    The data for this study was taken from the Xiàndài Hànyǔ Fāngyán
    Yīnkù (Hou 2004).
    21 / 30

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  37. Modelling Chinese Dialect History Data
    Data
    The data for this study was taken from the Xiàndài Hànyǔ Fāngyán
    Yīnkù (Hou 2004).
    It consists of 180 items (“meanings”) translated into 40
    contemporary Chinese dialects.
    21 / 30

    View Slide

  38. Modelling Chinese Dialect History Data
    Data
    The data for this study was taken from the Xiàndài Hànyǔ Fāngyán
    Yīnkù (Hou 2004).
    It consists of 180 items (“meanings”) translated into 40
    contemporary Chinese dialects.
    The data is available on a CD in RTF format along with recordings
    for all dialect entries.
    21 / 30

    View Slide

  39. Modelling Chinese Dialect History Data
    Data
    The data for this study was taken from the Xiàndài Hànyǔ Fāngyán
    Yīnkù (Hou 2004).
    It consists of 180 items (“meanings”) translated into 40
    contemporary Chinese dialects.
    The data is available on a CD in RTF format along with recordings
    for all dialect entries.
    For this study, the transcriptions in RTF were converted to Unicode.
    21 / 30

    View Slide

  40. Modelling Chinese Dialect History Data
    Data
    The data for this study was taken from the Xiàndài Hànyǔ Fāngyán
    Yīnkù (Hou 2004).
    It consists of 180 items (“meanings”) translated into 40
    contemporary Chinese dialects.
    The data is available on a CD in RTF format along with recordings
    for all dialect entries.
    For this study, the transcriptions in RTF were converted to Unicode.
    Every word was compared with the recordings in order to minimize
    errors resulting from the extraction process and the original
    encoding itself.
    21 / 30

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  41. Modelling Chinese Dialect History Data
    Data
    ITEM 太阳 tàiyáng “sun”
    .
    Dialect Pronunciation Characters Cognacy
    Shanghai tʰa³⁴⁻³³ɦiã¹³⁻⁴⁴ 太阳 1
    Shanghai ȵjɪʔ¹⁻¹¹dɤ¹³⁻²³ 日头 2
    Wenzhou tʰa⁴²⁻²²ji 太阳 1
    Wenzhou ȵi²¹³⁻²²dɤu 日头 2
    Guangzhou jit²tʰɐu²¹⁻³⁵ 热头 3
    Guangzhou tʰai³³jœŋ²¹ 太阳 1
    Haikou zit³hau³¹ 日头 2
    Beijing tʰai⁵¹iɑŋ¹ 太阳 1
    22 / 30

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  42. Modelling Chinese Dialect History Data
    Data
    01
    02
    03
    04
    05
    06
    07
    08
    09
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    31
    32
    33
    34
    35
    36
    37
    38
    39
    40
    Guanhua
    Jin
    Kejia
    Yue
    Gan
    Hui
    Min
    Xiang
    Wu
    Dialect Locations in the Xiàndài Hànyǔ Fāngyán Yīnkù
    01 Shanghai 上海
    02 Suzhou 苏州
    03 Hangzhou 杭州
    04 Wenzhou 温州
    05 Guangzhou 广州
    06 Nanning 南宁
    07 Xianggang 香港
    08 Xiamen 厦门
    09 Fuzhou 福州
    10 Jian'ou 建瓯
    11 Shantou 汕头
    12 Haikou 海口
    13 Taibei 台北
    14 Meixian 梅县
    15 Taoyuan 桃园
    16 Nanchang 南昌
    17 Changsha 长沙
    18 Xiangtan 湘潭
    19 Shexian 歙县
    20 Tunxi 屯溪
    21 Taiyuan 太原
    22 Pingyao 平遥
    23 Huhehaote 呼和浩特
    24 Beijing 北京
    25 Tianjin 天津
    26 Jinan 济南
    27 Qingdao 青岛
    28 Nanjing 南京
    29 Hefei 合肥
    30 Zhengzhou 郑州
    31 Wuhan 武汉
    32 Chengdu 成都
    33 Guiyang 贵阳
    34 Kunming 昆明
    35 Haerbin 哈尔滨
    36 Xi'an 西安
    37 Yinchuan 银川
    38 Lanzhou 兰州
    39 Xining 西宁
    40 Wulumuqi 乌鲁木齐
    01 Shanghai
    02 Suzhou
    03 Hangzhou
    04 Wenzhou
    05 Guangzhou
    06 Nanning
    07 Xianggang
    08 Xiamen
    09 Fuzhou
    10 Jian'ou
    11 Shantou
    12 Haikou
    13 Taibei
    14 Meixian
    15 Taoyuan
    16 Nanchang
    17 Changsha
    18 Xiangtan
    19 Shexian
    20 Tunxi
    21 Taiyuan
    22 Pingyao
    23 Huhehaote
    24 Beijing
    25 Tianjin
    26 Jinan
    27 Qingdao
    28 Nanjing
    29 Hefei
    30 Zhengzhou
    31 Wuhan
    32 Chengdu
    33 Guiyang
    34 Kunming
    35 Haerbin
    36 Xi'an
    37 Yinchuan
    38 Lanzhou
    39 Xining
    40 Wulumuqi
    23 / 30

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  43. Modelling Chinese Dialect History Analysis
    Analysis
    24 / 30

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  44. Modelling Chinese Dialect History Analysis
    Analysis
    The data was analyzed with help of Dagan and Martin’s (2008)
    method for phylogenetic network reconstruction, that was applied
    to linguistic data before (Nelson-Sathi et al. 2011).
    24 / 30

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  45. Modelling Chinese Dialect History Analysis
    Analysis
    The data was analyzed with help of Dagan and Martin’s (2008)
    method for phylogenetic network reconstruction, that was applied
    to linguistic data before (Nelson-Sathi et al. 2011).
    Given a binary reference tree reflecting the vertical history of a
    language family and a list of homologs (“cognates”) distributed
    over the languages, the method reconstructs horizontal relations
    between the languages and the internal nodes of the tree.
    24 / 30

    View Slide

  46. Modelling Chinese Dialect History Analysis
    Analysis
    The data was analyzed with help of Dagan and Martin’s (2008)
    method for phylogenetic network reconstruction, that was applied
    to linguistic data before (Nelson-Sathi et al. 2011).
    Given a binary reference tree reflecting the vertical history of a
    language family and a list of homologs (“cognates”) distributed
    over the languages, the method reconstructs horizontal relations
    between the languages and the internal nodes of the tree.
    The reconstruction of horizontal relations is done by seeking
    specific evolutionary models (loss and gain of characters) that fit
    the given distribution best.
    24 / 30

    View Slide

  47. Modelling Chinese Dialect History Analysis
    Analysis
    The data was analyzed with help of Dagan and Martin’s (2008)
    method for phylogenetic network reconstruction, that was applied
    to linguistic data before (Nelson-Sathi et al. 2011).
    Given a binary reference tree reflecting the vertical history of a
    language family and a list of homologs (“cognates”) distributed
    over the languages, the method reconstructs horizontal relations
    between the languages and the internal nodes of the tree.
    The reconstruction of horizontal relations is done by seeking
    specific evolutionary models (loss and gain of characters) that fit
    the given distribution best.
    The main criterion by which the fitness of the distributions is
    evaluated is the “vocabulary size”, i.e. the distribution of word
    forms over a set of meanings. Comparing the vocabulary sizes of
    different models that infer different amounts of lateral events, the
    model that comes closest to the vocabulary sizes of the
    contemporary languages is chosen.
    24 / 30

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  48. Modelling Chinese Dialect History Analysis
    Analysis
    Xi’an
    Zhengzhou
    Harbin
    Yinchuan
    Lanzhou
    Xining
    Qingdao
    Beijing
    Tunxi
    Hangzhou
    Suzhou
    Shanghai
    Wenzhou
    Tianjin
    Jinan Shexian
    Hefei
    Nanjing
    Wulumuqi
    Guiyang
    Wuhan
    Xiangtan
    Changsha
    Huhehaote
    Pingyao
    Taiyuan
    Kunming
    Chengdu
    Haikou
    Fuzhou
    Jian’ou
    Guangzhou
    Xianggang
    Shantou
    Xiamen
    Taibei
    Nanning
    Taoyuan
    Nanchang
    Meixian
    “sun” 日头 rìtou
    25 / 30

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  49. Modelling Chinese Dialect History Analysis
    Analysis
    Xi’an
    Zhengzhou
    Harbin
    Yinchuan
    Lanzhou
    Xining
    Qingdao
    Beijing
    Tunxi
    Hangzhou
    Suzhou
    Shanghai
    Wenzhou
    Tianjin
    Jinan Shexian
    Hefei
    Nanjing
    Wulumuqi
    Guiyang
    Wuhan
    Xiangtan
    Changsha
    Huhehaote
    Pingyao
    Taiyuan
    Kunming
    Chengdu
    Haikou
    Fuzhou
    Jian’ou
    Guangzhou
    Xianggang
    Taoyuan
    Nanchang
    Meixian
    Shantou
    Xiamen
    Taibei
    Nanning
    “sun” 太阳 tàiyáng
    25 / 30

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  50. Modelling Chinese Dialect History Analysis
    Analysis
    Xi’an
    Zhengzhou
    Harbin
    Yinchuan
    Lanzhou
    Xining
    Qingdao
    Beijing
    Tunxi
    Hangzhou
    Suzhou
    Shanghai
    Wenzhou
    Tianjin
    Jinan Shexian
    Hefei
    Nanjing
    Wulumuqi
    Guiyang
    Wuhan
    Xiangtan
    Changsha
    Huhehaote
    Pingyao
    Taiyuan
    Kunming
    Chengdu
    Haikou
    Fuzhou
    Jian’ou
    Guangzhou
    Xianggang
    Taoyuan
    Nanchang
    Meixian
    Shantou
    Xiamen
    Taibei
    Nanning
    “become sick” 生病 shēngbìng
    25 / 30

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  51. Modelling Chinese Dialect History Analysis
    Analysis
    Xi’an
    Zhengzhou
    Harbin
    Xining
    Yinchuan
    Lanzhou
    Qingdao
    Beijing
    Tunxi
    Hangzhou
    Suzhou
    Shanghai
    Wenzhou
    Tianjin
    Jinan Shexian
    Hefei
    Nanjing
    Wulumuqi
    Guiyang
    Wuhan
    Xiangtan
    Changsha
    Huhehaote
    Pingyao
    Taiyuan
    Kunming
    Chengdu
    Haikou
    Fuzhou
    Jian’ou
    Guangzhou
    Xianggang
    Taoyuan
    Nanchang
    Meixian
    Shantou
    Xiamen
    Taibei
    Nanning
    “aubergine” 茄子 qiézi
    25 / 30

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  52. Modelling Chinese Dialect History Results
    Results
    0
    200
    400
    600
    800
    1000
    1200
    Genome size
    p<0.05 p<0.05 p<0.05 p=0.2 p<0.05 p<0.05
    26 / 30

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  53. Modelling Chinese Dialect History Results
    Results
    The BOR3-model fits the distribution best. It allows up to three
    lateral connections per homolog.
    Out of 1152 homologs distributed over the Chinese dialects, 264
    are monophyletic, 328 require one, 355 two, and 177 three lateral
    links in order to explain the distribution neatly.
    This corresponds to a borrowing rate of 0.5286 borrowing events
    per homolog per lifetime.
    For 78 percent of all homologs in the dataset the method
    reconstructs lateral links and therefore suggests that these have
    been involved in borrowing events during their history.
    Suprisingly, the 48 homologs that correspond to basic vocabulary
    concepts in the dataset do not show significant differences in their
    borrowing rates compared to the non-basic items.
    26 / 30

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  54. Modelling Chinese Dialect History Results
    Results: General Results
    Nanjing
    Wuhan
    Hefei
    Guiyang
    Xining
    Zhengzhou
    Yinchuan
    Lanzhou
    Wulumuqi
    Xi’an
    Qingdao
    Tianjin
    Wenzhou
    Jinan
    Kunming
    Chengdu
    Taiyuan
    Harbin
    Beijing Nanchang
    Tunxi
    Taoyuan
    Meixian
    Shantou
    Xiamen
    Taibei
    Guangzhou
    Nanning
    Xianggang
    Huhehaote
    Pingyao
    Xiangtan
    Shanghai
    Suzhou
    Hangzhou
    Shexian
    Fuzhou
    Changsha
    Jian’ou
    Haikou
    Whole Dataset
    27 / 30

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  55. Modelling Chinese Dialect History Results
    Results: General Results
    Nanjing
    Wuhan
    Hefei
    Guiyang
    Xining
    Zhengzhou
    Yinchuan
    Lanzhou
    Wulumuqi
    Xi’an
    Qingdao
    Tianjin
    Wenzhou
    Jinan
    Kunming
    Chengdu
    Taiyuan
    Harbin
    Beijing Nanchang
    Tunxi
    Taoyuan
    Meixian
    Shantou
    Xiamen
    Taibei
    Guangzhou
    Nanning
    Xianggang
    Huhehaote
    Pingyao
    Xiangtan
    Shanghai
    Suzhou
    Hangzhou
    Shexian
    Fuzhou
    Changsha
    Jian’ou
    Haikou
    Swadesh Subset
    27 / 30

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  56. Modelling Chinese Dialect History Results
    Results: General Results
    Nanchang
    Shexian
    Tunxi
    Taoyuan
    Shanghai
    Suzhou
    Hangzhou
    Huhehaote
    Changsha
    Pingyao
    Jian’ou
    Xiangtan
    Fuzhou
    Haikou
    Meixian
    Xiamen
    Taibei
    Guangzhou
    Shantou
    Nanning
    Xianggang
    Wulumuqi
    Yinchuan
    Lanzhou
    Xining
    Zhengzhou
    Xi’an
    Chengdu
    Kunming
    Taiyuan
    Beijing
    Qingdao
    Harbin
    Tianjin
    Wenzhou
    Jinan
    Nanjing
    Wuhan
    Hefei
    Guiyang
    Whole Dataset (Cutoff 5)
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  57. Modelling Chinese Dialect History Results
    Results: General Results
    Nanchang
    Shexian
    Tunxi
    Taoyuan
    Shanghai
    Suzhou
    Hangzhou
    Huhehaote
    Changsha
    Pingyao
    Jian’ou
    Xiangtan
    Fuzhou
    Haikou
    Meixian
    Xiamen
    Taibei
    Guangzhou
    Shantou
    Nanning
    Xianggang
    Wulumuqi
    Yinchuan
    Lanzhou
    Xining
    Zhengzhou
    Xi’an
    Chengdu
    Kunming
    Taiyuan
    Beijing
    Qingdao
    Harbin
    Tianjin
    Wenzhou
    Jinan
    Nanjing
    Wuhan
    Hefei
    Guiyang
    Whole Dataset (Cutoff 10)
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  58. Modelling Chinese Dialect History Results
    Results: Chengdu
    Haikou
    Changsha
    Fuzhou
    Wuhan
    Xianggang
    Xiangtan
    Taoyuan
    Qingdao
    Zhengzhou
    Xi’an
    Pingyao
    Tianjin
    Taiyuan
    Lanzhou
    Yinchuan
    Jinan
    Wulumuqi
    Xining
    Huhehaote
    Harbin
    Beijing
    Shanghai
    Suzhou
    Hangzhou
    Shexian
    Hefei
    Wenzhou
    Tunxi
    Jian’ou
    Chengdu
    Nanjing
    Nanchang
    Guangzhou
    Nanning
    Meixian
    Xiamen
    Taibei
    Kunming
    Shantou
    Guiyang
    Contemporary Links Mapped to Coordinates
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  59. Modelling Chinese Dialect History Results
    Results: Chengdu
    Haikou
    Changsha
    Fuzhou
    Wuhan
    Xianggang
    Xiangtan
    Taoyuan
    Qingdao
    Zhengzhou
    Xi’an
    Pingyao
    Tianjin
    Taiyuan
    Lanzhou
    Yinchuan
    Jinan
    Wulumuqi
    Xining
    Huhehaote
    Harbin
    Beijing
    Shanghai
    Suzhou
    Hangzhou
    Shexian
    Hefei
    Wenzhou
    Tunxi
    Jian’ou
    Chengdu
    Nanjing
    Nanchang
    Guangzhou
    Nanning
    Meixian
    Xiamen
    Taibei
    Kunming
    Shantou
    Guiyang
    Contemporary Links of Chengdu
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  60. Modelling Chinese Dialect History Results
    Results: Chengdu
    Xiamen
    Shantou
    Taibei
    Meixian
    Xianggang
    Guangzhou
    Nanning
    Jian’ou
    Changsha
    Haikou
    Huhehaote
    Fuzhou
    Xiangtan
    Suzhou
    Hangzhou
    Tunxi
    Nanchang
    Taoyuan
    Shexian
    Guiyang
    Chengdu
    Kunming
    Taiyuan
    Pingyao
    Qingdao
    Tianjin
    Shanghai
    Wenzhou
    Beijing
    Jinan
    Xining
    Zhengzhou
    Lanzhou
    Yinchuan
    Harbin
    Xi’an
    Wuhan
    Hefei
    Nanjing
    Wulumuqi
    Links of Chengdu
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  61. Modelling Chinese Dialect History Results
    Results: Nanchang
    Nanchang
    Tunxi
    Taoyuan
    Shexian
    Suzhou
    Hangzhou
    Jian’ou
    Xiangtan
    Huhehaote
    Changsha
    Fuzhou
    Haikou
    Guangzhou
    Nanning
    Shantou
    Xiamen
    Meixian
    Xianggang
    Taibei
    Yinchuan
    Lanzhou
    Xining
    Kunming
    Pingyao
    Taiyuan
    Chengdu
    Qingdao
    Tianjin
    Wenzhou
    Shanghai
    Beijing
    Jinan
    Wuhan
    Hefei
    Wulumuqi
    Nanjing
    Guiyang
    Harbin
    Zhengzhou
    Xi’an
    Links of Nanchang
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  62. Modelling Chinese Dialect History Results
    Results: Nanchang
    Haikou
    Changsha
    Fuzhou
    Wuhan
    Xianggang
    Xiangtan
    Taoyuan
    Qingdao
    Zhengzhou
    Xi’an
    Pingyao
    Tianjin
    Taiyuan
    Lanzhou
    Yinchuan
    Jinan
    Wulumuqi
    Xining
    Huhehaote
    Harbin
    Beijing
    Shanghai
    Suzhou
    Hangzhou
    Shexian
    Hefei
    Wenzhou
    Tunxi
    Jian’ou
    Chengdu
    Nanjing
    Nanchang
    Guangzhou
    Nanning
    Meixian
    Xiamen
    Taibei
    Kunming
    Shantou
    Guiyang
    Contemporary Links of Nanchang
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  63. Modelling Chinese Dialect History Results
    Results: Nanchang
    Shanghai
    Nanjing
    Suzhou
    Hangzhou
    Hefei
    Shexian
    Tunxi
    Wenzhou
    Wuhan
    Xiangtan
    Changsha
    Jian’ou
    Nanchang
    Links between Nanchang and its Neighbors
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  64. Concluding Remarks
    Phylogenetic networks look nice.
    Phylogenetic networks are – if properly reconstructed – a valid
    alternative to both the tree and the wave model.
    We need to test the method by Dagan and Martin (2008) on more
    data and in more detail in order to be able to give an account on its
    full potential and its limits.
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  65. Concluding Remarks
    谢谢大家!
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  66. Concluding Remarks
    Thank you!
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