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Network Approaches to the Reconstruction of Old Chinese Phonology

Network Approaches to the Reconstruction of Old Chinese Phonology

Talk held at the Center for Chinese Linguistics (The Hong Kong University of Science and Technology, Hong Kong).

Johann-Mattis List

March 07, 2017
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  1. Network Approaches to the Reconstruction of Old
    Chinese Phonology
    Johann-Mattis List
    Department of Linguistic and Cultural Evolution
    Max Planck Institute for the Science of Human History
    Jena
    2017/03/07
    1 / 42

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  2. Introduction
    Introduction
    2 / 42

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  3. Introduction Rhyming in General
    Rhyming in General
    Lose yourself in the music
    the moment you own it
    you better never let it go
    you only get one shot
    do not
    miss your chance to blow
    this opportunity comes once in a lifetime…
    (Eminem, “Lose yourself”, 2002)
    3 / 42

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  4. Introduction Rhyming in General
    Rhyming in General
    Lose yourself in the music
    the moment you own it
    you better never let it go
    you only get one shot
    do not
    miss your chance to blow
    this opportunity comes once in a lifetime…
    (Eminem, “Lose yourself”, 2002)
    3 / 42

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  5. Introduction Rhyming in General
    Rhyming in General
    Lose yourself in the music [-ɪk] ? [ɔi]
    the moment you own it [-ɪt] ? [ai]
    you better never let it go
    you only get one shot
    do not
    miss your chance to blow
    this opportunity comes once in a lifetime…
    (Eminem, “Lose yourself”, 2002)
    3 / 42

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  6. Introduction Rhyming in General
    Rhyming in General
    music [-ɪk]
    own it [-ɪt]
    But Germans would rhyme employ and deny!
    Mai [-ɔi]
    neu [-ai]
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  7. Introduction Rhyming as Evidence
    Rhyming as Evidence
    燕 燕 于 飛, 下 上 其 音。 The swallows go flying, falling and rising
    are their voices;
    yān yān yú fēi xià shàng qí yīn
    之 子 于 歸, 遠 送 于 南。 This young lady goes to her new home,
    far I accompany her to the south.
    zhī zǐ yú guī, yuǎn sòng yú nán
    瞻 望 弗 及, 實 勞 我 心。 I gaze after her, can no longer see her,
    truly it grieves my heart.
    zhān wàng fú jí, shí láo wǒ xīn
    4 / 42

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  8. Introduction Rhyming as Evidence
    Rhyming as Evidence
    燕 燕 于 飛, 下 上 其 音。 The swallows go flying, falling and rising
    are their voices;
    yān yān yú fēi xià shàng qí yīn
    之 子 于 歸, 遠 送 于 南。 This young lady goes to her new home,
    far I accompany her to the south.
    zhī zǐ yú guī, yuǎn sòng yú nán
    瞻 望 弗 及, 實 勞 我 心。 I gaze after her, can no longer see her,
    truly it grieves my heart.
    zhān wàng fú jí, shí láo wǒ xīn
    4 / 42

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  9. Introduction Rhyming as Evidence
    Rhyming as Evidence
    燕 燕 于 飛, 下 上 其 音。
    yān yān yú *pər xià shàng qí *qrəm
    之 子 于 歸, 遠 送 于 南。
    zhī zǐ yú *kʷəj, yuǎn sòng yú *nˤəm
    瞻 望 弗 及, 實 勞 我 心。
    zhān wàng fú jí, shí láo wǒ *səm
    4 / 42

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  10. Introduction Rhyming as Evidence
    Rhymes as Evidence
    Chinese Text RW MCH Gù Yánwǔ Wáng (1980) Baxter (1992) OCBS-R
    殷其靁 léi 靁 *lwoj 靁 A, 之部 靁 - 靁 - *-uj
    在南山之陽 yáng 陽 *yang 陽 B, 陽部 陽 A, jiang, 陽部 陽 A, *ljang *-aŋ
    何斯違斯 sī 斯 *sje 斯 A, 之部 斯 - 斯 - *-e
    莫敢或遑 huáng 遑 *hwang 遑 B, 陽部 遑 A, huang, 陽部 遑 A, *wang *-aŋ
    振振君子 zǐ 子 *tsiX 子 A, 之部 子 B, tziə, 之部 子 - *-əʔ
    歸哉歸哉 zāi 哉 *tsoj 哉 A, 之部 哉 B, tzə, 之部 哉 - *-ə
    Comparing differences in rhyme identification for Ode 19.1《周南·殷其雷》
    5 / 42

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  11. Introduction Rhyming as Evidence
    Rhymes as Evidence
    We have great difficulty to judge rhyme evidence,
    specifically, for unattested, reconstructed languages,
    as we cannot directly query the speakers. Given that
    poetry in general is influenced by multiple factors, in-
    cluding the language system, the structure of the soci-
    ety, and human cognition in general, it is difficult for us
    to disentangle which factors we are dealing with under
    which situations.
    6 / 42

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  12. Introduction Old Chinese Phonology
    Old Chinese Phonology
    long tradition of linguistic reconstruction in China (starting with
    Chén Dì 陳第, 1541 – 1606),
    7 / 42

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  13. Introduction Old Chinese Phonology
    Old Chinese Phonology
    long tradition of linguistic reconstruction in China (starting with
    Chén Dì 陳第, 1541 – 1606),
    breakthrough in the early 20th century with Karlgren’s
    reconstructions and impressive work by Wáng Lí 王力 (1980) and
    Li Fang-kuei 李方桂 (1971),
    7 / 42

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  14. Introduction Old Chinese Phonology
    Old Chinese Phonology
    long tradition of linguistic reconstruction in China (starting with
    Chén Dì 陳第, 1541 – 1606),
    breakthrough in the early 20th century with Karlgren’s
    reconstructions and impressive work by Wáng Lí 王力 (1980) and
    Li Fang-kuei 李方桂 (1971),
    since then more and more improved concrete reconstructions of
    Old Chinese phonology,
    7 / 42

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  15. Introduction Old Chinese Phonology
    Old Chinese Phonology
    long tradition of linguistic reconstruction in China (starting with
    Chén Dì 陳第, 1541 – 1606),
    breakthrough in the early 20th century with Karlgren’s
    reconstructions and impressive work by Wáng Lí 王力 (1980) and
    Li Fang-kuei 李方桂 (1971),
    since then more and more improved concrete reconstructions of
    Old Chinese phonology,
    another breakthrough in the 1980s, when Baxter (1992), Starostin
    (1989), and Zhèngzhāng Shàngfāng (see Zhèngzhāng 2003)
    presented reconstructions in which they independently proposed
    several similar features (notably six vowels and more rhymes than
    proposed in the classical analysis)
    7 / 42

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  16. Introduction Old Chinese Phonology
    Old Chinese Phonology
    As a general problem, Old Chinese phonology, al-
    though representing a highly data-driven discipline, has
    so far paid little attention to the problem of making lin-
    guistic data transparently available and comparable.
    Thus, there exist many different reconstructions for Old
    Chinese, as well as many different rhyme annotations
    of the Book of Odes (Shījīng 詩經), but they have never
    been compared on a large scale. If disciplines rely on
    multiple different types of evidence, it is crucial that this
    evidence is handled in a comparable and principled
    way.
    8 / 42

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  17. Networks
    Networks
    9 / 42

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  18. Networks Networks in General
    Networks in General
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  19. Networks Networks in General
    Networks in General
    NODE (VERTEX)
    represents an
    object
    10 / 42

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  20. Networks Networks in General
    Networks in General
    NODE (VERTEX)
    represents an
    object
    EDGE (LINK)
    represents a
    relation between
    objects
    10 / 42

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  21. Networks Networks in General
    Networks in General
    can be
    tagged or
    labelled
    EDGE (LINK)
    represents a
    relation between
    objects
    10 / 42

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  22. Networks Networks in General
    Networks in General
    can be
    tagged or
    labelled
    can be
    labelled and
    weighted
    10 / 42

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  23. Networks Network Examples
    Network Examples
    Many structures in daily life and science can be modeled as
    networks:
    social networks: nodes are persons, edges are relations between
    persons (e.g., friendship on FaceBook, etc.),
    11 / 42

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  24. Networks Network Examples
    Network Examples
    Many structures in daily life and science can be modeled as
    networks:
    social networks: nodes are persons, edges are relations between
    persons (e.g., friendship on FaceBook, etc.),
    phylogenetic networks: nodes are languages or dialect varieties,
    edges represent genetic closeness,
    11 / 42

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  25. Networks Network Examples
    Network Examples
    Many structures in daily life and science can be modeled as
    networks:
    social networks: nodes are persons, edges are relations between
    persons (e.g., friendship on FaceBook, etc.),
    phylogenetic networks: nodes are languages or dialect varieties,
    edges represent genetic closeness,
    network of sound change patterns: nodes are sounds, directed
    edges represent likelihood of sound change during language
    evolution,
    11 / 42

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  26. Networks Network Examples
    Network Examples
    Many structures in daily life and science can be modeled as
    networks:
    social networks: nodes are persons, edges are relations between
    persons (e.g., friendship on FaceBook, etc.),
    phylogenetic networks: nodes are languages or dialect varieties,
    edges represent genetic closeness,
    network of sound change patterns: nodes are sounds, directed
    edges represent likelihood of sound change during language
    evolution,
    ...
    11 / 42

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  27. Networks Network Approaches
    Network Approaches
    With help of quantitative approaches, existing networks can
    be quickly analyzed and we can infer interesting things about
    their general structure or specific characteristics. The litera-
    ture on network approaches is abundant, and often it is dif-
    ficult for scientists to find the right way to tackle their prob-
    lems. For this reason it is useful to work in an interdisci-
    plinary and to discuss problems and questions of data han-
    dling and analysis with network specialists.
    12 / 42

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  28. Networks Network Approaches
    Network Approaches
    13 / 42

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  29. Networks Network Approaches
    Network Approaches
    13 / 42

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  30. Networks Network Approaches
    Network Approaches
    13 / 42

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  31. Networks Network Approaches
    Network Approaches
    13 / 42

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  32. Networks Network Approaches
    Network Approaches
    13 / 42

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  33. Networks Network Approaches
    Network Approaches
    Methods for community detection are a specific type of par-
    titioning algorithm. They allow us to partition (cluster) the
    nodes of a network into different parts. Community detec-
    tion algorithms are very useful to detect natural groupings in
    networks.
    14 / 42

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  34. Rhymes and Networks
    Rhymes and Networks
    15 / 42

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  35. Rhymes and Networks Modeling
    Modeling
    16 / 42

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  36. Rhymes and Networks Modeling
    Modeling
    16 / 42

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  37. Rhymes and Networks Modeling
    Modeling
    16 / 42

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  38. Rhymes and Networks Modeling
    Modeling
    16 / 42

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  39. Rhymes and Networks Modeling
    Modeling
    16 / 42

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  40. Rhymes and Networks Modeling
    Modeling
    16 / 42

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  41. Rhymes and Networks Modeling
    Modeling
    27.3.A
    30.2.A
    33.3.A
    39.1.A
    54.4.B
    58.1.A
    58.6.B
    59.1.A
    66.1.A
    130.1.A
    204.4.A
    227.2.A
    sī 丝
    qī 淇 móu 谋
    qī 淇
    qī 淇
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    zhī 之
    zhī 之
    qī 期
    qī 期
    méi 梅
    méi 梅
    yóu 尤
    yóu 尤
    lái 来 sī 思
    lái 来
    lái 来
    sī 思
    sī 思
    sī 思
    sī 思
    sī 思
    sī 思
    sī 丝
    sī 丝 sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝 sī 丝
    sī 丝 sī 丝
    sī 丝 sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    sī 丝
    móu 谋
    zāi 哉
    zāi 哉
    zāi 哉
    zāi 哉
    16 / 42

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  42. Rhymes and Networks Modeling
    Modeling












    16 / 42

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  43. Rhymes and Networks Modeling
    Modeling
    Poem Stanza Verse Sect. Text Rhyme Pattern MCH OCBS
    4 1 1 1 南有樛木、 木 - muwk C.mˤok
    4 1 1 2 葛藟纍之。 纍 A lwij [r]uj
    4 1 2 1 樂只君子、 子 - tsiX tsəʔ
    4 1 2 2 福履綏之。 綏 A swij s.nuj
    4. 樛木
    南有樛木、葛藟纍之。
    樂只君子、福履綏之。
    南有樛木、葛藟荒之。
    樂只君子、福履將之。
    南有樛木、葛藟縈之。
    樂只君子、福履成之。
    17 / 42

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  44. Rhymes and Networks Modeling
    Modeling
    Reconstructing an initial rhyme network for poems which are
    annotated for their rhyme relations is straightforward. Prob-
    lematic is the weighting of recurring rhyme connections, the
    treatment of larger poems, and the handling of specific types
    of rhyme (rhymes inside the same line, rhymes of bi-syllabic
    units, etc.).
    18 / 42

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  45. Rhymes and Networks Modeling
    Modeling
    if two characters rhyme more than one time throughout a collection
    of poems, this is of course stronger evidence for their similarity
    than a spurious rhyming, and it should be handled in the model (→
    use weighted networks to represent frequency)
    if words rhyme in larger stanzas, these stanzas contain more
    words, and accordingly, more units will rhyme with more other
    units, which may overweight the closeness between the rhyme
    words in a longer stanza (→ normalize rhyme connections in each
    stanza)
    line-internal rhymes and bi-syllabic rhymes follow at times different
    rules (especially the former), and ideally their modeling would
    reflect them as such (question not yet solved in the current
    application)
    19 / 42

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  46. Rhymes and Networks Modeling
    Example: The Shījīng Rhyme Browser
    interactive web-based application
    displays Shījīng rhymes in digitized form with rhyme annotations
    following Baxter (1992) and rhyme readings following Baxter and
    Sagart (2014) and Pān (2000, as provided in the Thesaurus
    Linguae Sericae).
    offers a quick and transparent way to inspect Baxter’s rhyme
    annotations, as well as a quick way to search through the Shījīng
    for rhyme patterns and brief glosses.
    URL: http://digling.org/shijing
    20 / 42

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  47. Rhymes and Networks R-Hypothesis
    R-Hypothesis (List forthcoming)
    theory first laid out by Starostin (1989: 338-340) who claimed that
    apparent rhyme connections between *-n and *-j-codas can be
    explained by reconstructing an additional coda *-r whose normal
    reflex in Middle Chinese was -n,
    21 / 42

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  48. Rhymes and Networks R-Hypothesis
    R-Hypothesis (List forthcoming)
    theory first laid out by Starostin (1989: 338-340) who claimed that
    apparent rhyme connections between *-n and *-j-codas can be
    explained by reconstructing an additional coda *-r whose normal
    reflex in Middle Chinese was -n,
    further evidence was provided in presumed cognates from Tibetan
    (Hill 2014),
    21 / 42

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  49. Rhymes and Networks R-Hypothesis
    R-Hypothesis (List forthcoming)
    theory first laid out by Starostin (1989: 338-340) who claimed that
    apparent rhyme connections between *-n and *-j-codas can be
    explained by reconstructing an additional coda *-r whose normal
    reflex in Middle Chinese was -n,
    further evidence was provided in presumed cognates from Tibetan
    (Hill 2014),
    Baxter and Sagart (2014) followed this idea in their new
    reconstruction for Old Chinese, but they did not test the hypothesis
    on the rhymes
    21 / 42

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  50. Rhymes and Networks R-Hypothesis
    R-Hypothesis (List forthcoming)
    theory first laid out by Starostin (1989: 338-340) who claimed that
    apparent rhyme connections between *-n and *-j-codas can be
    explained by reconstructing an additional coda *-r whose normal
    reflex in Middle Chinese was -n,
    further evidence was provided in presumed cognates from Tibetan
    (Hill 2014),
    Baxter and Sagart (2014) followed this idea in their new
    reconstruction for Old Chinese, but they did not test the hypothesis
    on the rhymes
    Can we use the Shījīng network data to provide additional tests for
    this hypothesis?
    21 / 42

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  51. Rhymes and Networks R-Hypothesis
    R-Hypothesis: Full Shījīng Network
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    B
    A
    22 / 42

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  52. Rhymes and Networks R-Hypothesis
    R-Hypothesis: Full Shījīng Network
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    *-əj
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    22 / 42

    View full-size slide

  53. Rhymes and Networks R-Hypothesis
    R-Hypothesis: Community Detection
    By eyeballing the network alone, we cannot find any conclu-
    sive proof for any of the finals of Old Chinese. Instead, we
    need to partition the data, using classical methods for com-
    munity detection. If these clusters, inferred from the network
    structure, reflect the distinction of OC *-j and *-n words into
    three classes, this can be seen as strong evidence for the *-r
    coda.
    23 / 42

    View full-size slide

  54. Rhymes and Networks R-Hypothesis
    R-Hypothesis: Community Detection
    community detection with help of Infomap (Rosvall and Bergstrom
    2007)
    algorithm splits the Shījīng network into 345 distinct communities
    all data can be interactively browsed at
    http://digling.org/shijing/communities.html
    24 / 42

    View full-size slide

  55. Rhymes and Networks R-Hypothesis
    R-Hypothesis: Power of Community Detection
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    *-ə
    *-əʔ
    25 / 42

    View full-size slide

  56. Rhymes and Networks R-Hypothesis
    R-Hypothesis: Power of Community Detection
    Comparing the -əʔ vs. the -ə-coda, as inferred by the algo-
    rithm, the inference comes very close to the reconstruction
    system of Baxter and Sagart (2014): of 74 rhyme words as-
    signed to -əʔ automatically, 59 also reconstructed as such;
    of 39 rhyme words automatically assigned to -ə, 30 are re-
    constructed as such in Baxter and Sagart.
    25 / 42

    View full-size slide

  57. Rhymes and Networks R-Hypothesis
    R-Hypothesis: Testing the *-a[nrj] Coda
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    26 / 42

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  58. Rhymes and Networks R-Hypothesis
    R-Hypothesis: Testing the *-a[nrj] Coda
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    媛 澗
    我 儺
    詈 左
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    B
    26 / 42

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  59. Rhymes and Networks R-Hypothesis
    R-Hypothesis: Testing the *-a[nrj] Coda
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    D
    26 / 42

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  60. Rhymes and Networks R-Hypothesis
    R-Hypothesis: Testing the *-a[nrj] Coda
    Thanks to the community analysis on the Shījīng rhyme net-
    work, we can feed back to Baxter and Sagart’s reconstruc-
    tion and conform many cases of uncertainty. The structure
    of the clusters indicates that the r-coda can also be detected
    in the rhyme networks. More research is needed to include
    the results of the automatic analysis into the reconstruction
    of Old Chinese.
    26 / 42

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  61. Rhymes and Networks Vowel Purity
    Vowel Purity (List et al. forthcoming)
    Ho (2016) claims that the principle of vowel purity was im-
    portant in Old Chinese rhyming: poets would try to avoid
    rhyming words with different consonants, while differences
    in the codas were more often tolerated. Reconstruction sys-
    tems which contradict this principle, may therefore be exter-
    nally criticized as neglecting the principle of vowel purity. On
    the other hand, we can compare different reconstruction re-
    garding the degree of purity of their vowels compared to the
    rhyme data in the Shījīng.
    27 / 42

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  62. Rhymes and Networks Vowel Purity
    Vowel Purity: Comparative Data
    Reconstruction System No. Rhymes Density a ɑ æ e ə o ɔ u ʊ ɯ i
    Karlgren (1957) 1830 0.0031 0.0026 x x x x x x x x x x x
    Li 李方桂 (1971) 1830 0.0031 0.0026 x x x x
    Wáng 王力 (1980) 1830 0.0031 0.0026 x x x x x
    Zhèngzhāng 鄭張尚芳 (2003) 1830 0.0031 0.0030 x x x x x x
    Starostin (1989) 1358 0.0035 0.0026 x x x x x x
    Pān 潘悟雲 (2000) 1830 0.0031 0.0026 x x x x x
    Baxter and Sagart (2014) 1431 0.0038 0.0033 x x x x x x
    Schuessler (2007) 1224 0.0041 0.0035 x x x x x x
    28 / 42

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  63. Rhymes and Networks Vowel Purity
    Vowel Purity: Method for Testing
    Assortativity tests whether nodes sharing connections in a
    graph are also similar regarding other characteristics (New-
    man 2003). In social network analyses it can, for exam-
    ple, be used to test whether observed patterns in a network,
    like friendship, come along with properties of the individu-
    als, such as language or gender (ibid.). Assortativity can
    be measured by calculating the assortativity coefficient of a
    network in which all nodes have a given attribute.
    29 / 42

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  64. Rhymes and Networks Vowel Purity
    Vowel Purity: Method for Testing
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    30 / 42

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  65. Rhymes and Networks Vowel Purity
    Vowel Purity: Method for Testing
    A 1 2 3 4 5 6 B 1 2 3 4 5 6
    1 x x 1 x x
    2 x x 2 x x
    3 x x x 3 x x x x
    4 x x x 4 x x x
    5 x x 5 x x x
    6 x x 6 x x x x
    30 / 42

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  66. Rhymes and Networks Vowel Purity
    Vowel Purity: Method for Testing
    A red blue red + blue B red blue red + blue
    red 6/14 = 0.43 1/14/ = 0.07 7/14 = 0.5 red 6/18 = 0.33 4/18/ = 0.22 10/18 = 0.55
    blue 1/14 = 0.07 6/14 = 0.43 7/14 = 0.5 blue 4/18 = 0.33 4/18 = 0.22 8/18 = 0.44
    red + blue 7/14 = 0.5 7/14 = 0.5 14/14 = 1.0 red + blue 10/18 = 0.55 8/18 = 0.44 18/18 = 1.0
    30 / 42

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  67. Rhymes and Networks Vowel Purity
    Vowel Purity: Results of the Tests
    Reconstruction System Assortativity Randomized
    Assortativity (Ø)
    Standard
    Deviation
    Sigma
    Score
    Rank p-
    Value
    Karlgren (1957) 0.5824 -0.0029 0.0091 64 6 < 0.001
    Li 李方桂 (1971) 0.8230 -0.0026 0.0149 56 8 < 0.001
    Wáng 王力 (1980) 0.7709 -0.0026 0.0127 61 7 < 0.001
    Zhèngzhāng 鄭張尚芳 (2003) 0.7435 -0.0021 0.0103 72 3 < 0.001
    Starostin (1989) 0.8444 -0.0025 0.0115 74 2 < 0.001
    Pān 潘悟雲 (2000) 0.7326 -0.0020 0.0103 71 4 < 0.001
    Baxter and Sagart (2014) 0.8765 -0.0025 0.0112 79 1 < 0.001
    Schuessler (2007) 0.7244 -0.0026 0.0111 66 5 < 0.001
    31 / 42

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  68. Rhymes and Networks Vowel Purity
    Vowel Purity: Results of the Tests
    Reconstruction System 100 # 200 # 300 # 400 # 500 # 600 # 700 # 800 #
    Karlgren (1954) 7 5 16 3 23 3 32 3 36 6 45 6 52 5 60 5
    Li 李方桂 (1971) 7 5 13 8 18 8 26 8 32 8 40 8 45 8 51 8
    Wáng 王力 (1980) 7 5 15 7 21 7 30 7 35 7 43 7 49 7 56 7
    Zhèngzhāng 鄭張尚芳 (2003) 9 2 16 3 23 3 32 3 40 3 49 3 55 3 64 3
    Starostin (1989) 9 2 17 2 25 2 35 2 43 2 51 2 59 2 68 2
    Pān 潘悟雲 (2000) 9 2 16 3 23 3 32 3 39 4 48 4 54 4 63 4
    Baxter and Sagart (2014) 10 1 18 1 27 1 37 1 46 1 55 1 63 1 72 1
    Schuessler (2007) 7 5 16 3 22 6 31 6 38 5 46 5 52 5 60 5
    31 / 42

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  69. Rhymes and Networks Vowel Purity
    Vowel Purity: Results of the Tests
    Provided the principle of vowel purity was really dominant
    during time of the creation of the Shījīng, our results indicate
    that reconstruction systems with six vowels outperform those
    with less or more vowels. Given that we do not know to which
    degree vowel purity was important in Old Chinese rhyming,
    this does not allow us to prove or disprove any of the recon-
    struction systems. Further research on rhyming practice and
    pragmatics are needed.
    31 / 42

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  70. More on Networks
    More Networks
    32 / 42

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  71. More on Networks Fǎnqiè Networks
    Fǎnqiè Networks
    It has long since been known that fǎnqiè 反切 readings can also be
    analyzed by exploiting their network characteristics (see, e.g.,
    Gěng Zhènshēng 耿振生 2004 on the fǎnqiè xìliánfǎ 反切系聯法).
    33 / 42

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  72. More on Networks Fǎnqiè Networks
    Fǎnqiè Networks
    It has long since been known that fǎnqiè 反切 readings can also be
    analyzed by exploiting their network characteristics (see, e.g.,
    Gěng Zhènshēng 耿振生 2004 on the fǎnqiè xìliánfǎ 反切系聯法).
    But with modern network approaches, we can handle the data
    more consistently and transparently.
    33 / 42

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  73. More on Networks Fǎnqiè Networks
    Fǎnqiè Networks
    It has long since been known that fǎnqiè 反切 readings can also be
    analyzed by exploiting their network characteristics (see, e.g.,
    Gěng Zhènshēng 耿振生 2004 on the fǎnqiè xìliánfǎ 反切系聯法).
    But with modern network approaches, we can handle the data
    more consistently and transparently.
    By extracting, for example, all fǎnqiè shàngzì 反切上字 from the
    Guǎngyùn 廣韻, we can create networks of fǎnqiè connections.
    33 / 42

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  74. More on Networks Fǎnqiè Networks
    Fǎnqiè Networks
    It has long since been known that fǎnqiè 反切 readings can also be
    analyzed by exploiting their network characteristics (see, e.g.,
    Gěng Zhènshēng 耿振生 2004 on the fǎnqiè xìliánfǎ 反切系聯法).
    But with modern network approaches, we can handle the data
    more consistently and transparently.
    By extracting, for example, all fǎnqiè shàngzì 反切上字 from the
    Guǎngyùn 廣韻, we can create networks of fǎnqiè connections.
    These networks are ideal for teaching Chinese traditional
    phonology, but also for comparison if scholars have different
    opinions.
    33 / 42

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  75. More on Networks Fǎnqiè Networks
    Fǎnqiè Networks
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    34 / 42

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  76. More on Networks Fǎnqiè Networks
    Fǎnqiè Networks
    1
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    姑 古






    詭 空


    34 / 42

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  77. More on Networks Fǎnqiè Networks
    Fǎnqiè Networks
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    kʰɐk
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    34 / 42

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  78. More on Networks Fǎnqiè Networks
    Fǎnqiè Networks
    Fǎnqiè networks are still underexplored, both with respect
    to traditional scholarship on Chinese historical phonology
    and with respect to the way they are best handled, and
    potential differences across Chinese rhyme books or other
    sources containing fǎnqiè readings from different epochs or
    authors. However, it seems promising to further exploit and
    test the approaches, as they may drastically increase the
    transparency of current approaches.
    35 / 42

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  79. More on Networks Xiéshēng Networks
    Xiéshēng Networks
    Since Duàn Yùcái 段玉裁 detected the strong correlation between
    the phonetic part of xíngshēng 形聲 characters, we know that the
    Chinese system basically reflects a network structure, since a large
    part of the characters can be decomposed into subparts which
    reflect other characters or recur across different characters.
    36 / 42

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  80. More on Networks Xiéshēng Networks
    Xiéshēng Networks
    Since Duàn Yùcái 段玉裁 detected the strong correlation between
    the phonetic part of xíngshēng 形聲 characters, we know that the
    Chinese system basically reflects a network structure, since a large
    part of the characters can be decomposed into subparts which
    reflect other characters or recur across different characters.
    Not often really taken into consideration is the historical aspect of
    these connections. Not all phonetic units of xíngshēng characters
    were formed at the same time, and the characters reflect a
    complex evolution of character formation at different steps.
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  81. More on Networks Xiéshēng Networks
    Xiéshēng Networks
    Since Duàn Yùcái 段玉裁 detected the strong correlation between
    the phonetic part of xíngshēng 形聲 characters, we know that the
    Chinese system basically reflects a network structure, since a large
    part of the characters can be decomposed into subparts which
    reflect other characters or recur across different characters.
    Not often really taken into consideration is the historical aspect of
    these connections. Not all phonetic units of xíngshēng characters
    were formed at the same time, and the characters reflect a
    complex evolution of character formation at different steps.
    Instead of listing xíngshēng series in form of lists of characters and
    common component, we should create explicit networks, as they
    are much more transparent to display where scholars disagree in
    their analyses, but also which characters are immediately
    composed of other characters.
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  82. More on Networks Xiéshēng Networks
    Xiéshēng Networks

    滂 訪








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  83. More on Networks Xiéshēng Networks
    Xiéshēng Networks
    Displaying character structures, especially aspects of char-
    acter formation, with help of directed networks could greatly
    benefit not only scientific exchange among scholars, who
    would be encouraged to present their judgments more trans-
    parently, but also other aspects of Chinese writing, such
    as, e.g., pedagogical aspects of teaching the structure of
    the writing system to beginners, or information-theoretic as-
    pects.
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  84. More on Networks Dynamic Networks
    Dynamic Networks
    Not only the data we model in networks can be enhanced,
    also our methods to analyze the networks need to be fur-
    ther improved. As an example, consider dynamic networks,
    which would analyze and model network changes in time. By
    improving on these methods, we could, for example, com-
    pare fǎnqiè networks across different epochs, as well as
    rhyme networks from different authors, dialects, and styles.
    We could further try to induce fundamental hierarchies and
    relative time frames from xiéshēng networks.
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  85. Outlook
    Outlook
    Outlook
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  86. Outlook
    One of the major insights that I made during the last years in
    my research is that despite the great achievements scholars
    have made in historical linguistics, and especially in Chinese
    traditional phonology, we still lack clear-cut frameworks that
    help us to produce our data transparently. Historical linguis-
    tics is a data-driven discipline, but scholars tend to ignore
    this when presenting their incredible insights in an intrans-
    parent form. Networks can help in two ways here: first, they
    are a transparent way of data-representation; and second,
    they provide an added value in those cases, where data be-
    comes too large for scholars to be inspected by eye-balling
    only.
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  87. Outlook
    多謝大家!
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