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Using networks to infer sound correspondence patterns across multiple languages

Using networks to infer sound correspondence patterns across multiple languages

Talk, held at the workshop "Distinguishability in Phylogenetic Networks" (Lorentz Center, Leiden, 2018-08-13-17).

Johann-Mattis List

August 16, 2018
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  1. Using networks to infer sound correspondence patterns
    across multiple languages
    Johann-Mattis List
    Research Group “Computer-Assisted Language Comparison”
    Department of Linguistic and Cultural Evolution
    Max-Planck Institute for the Science of Human History
    Jena, Germany
    2018-08-16
    very
    long
    title
    P(A|B)=P(B|A)...
    1 / 45

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  2. Comparative Linguistics
    2 / 45

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  3. "All languages change, as long as they exist."
    (August Schleicher 1863)
    walkman
    Indo-European
    Germanic
    Old English
    English
    p
    f
    f
    f
    ə
    a
    æ
    ɑː
    t
    d
    d
    ð


    e
    ə
    r
    r
    r
    r
    Germanic
    German English
    iPod
    Comparative Linguistics
    2 / 45

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  4. iPod
    Indo-European
    Germanic
    Old English
    English
    p
    f
    f
    f
    ə
    a
    æ
    ɑː
    t
    d
    d
    ð


    e
    ə
    r
    r
    r
    r
    Germanic
    German English
    walkman
    "All languages change, as long as they exist."
    (August Schleicher 1863)
    Comparative Linguistics
    2 / 45

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  5. walkman
    Indo-European
    Germanic
    Old English
    English
    p
    f
    f
    f
    ə
    a
    æ
    ɑː
    t
    d
    d
    ð


    e
    ə
    r
    r
    r
    r
    Germanic
    German English
    iPod
    "All languages change, as long as they exist."
    (August Schleicher 1863)
    Comparative Linguistics
    2 / 45

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  6. walkman
    Indo-European
    Germanic
    Old English
    English
    p
    f
    f
    f
    ə
    a
    æ
    ɑː
    t
    d
    d
    ð


    e
    ə
    r
    r
    r
    r
    Germanic
    German English
    iPod
    "All languages change, as long as they exist."
    (August Schleicher 1863)
    Comparative Linguistics
    2 / 45

    View full-size slide

  7. iPod
    Indo-European
    Germanic
    Old English
    English
    p
    f
    f
    f
    ə
    a
    æ
    ɑː
    t
    d
    d
    ð


    e
    ə
    r
    r
    r
    r
    walkman
    L₁
    L₁ L₁
    L₁
    L₁
    "All languages change, as long as they exist."
    (August Schleicher 1863)
    Comparative Linguistics
    2 / 45

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  8. iPod
    Indo-European
    Germanic
    Old English
    English
    p
    f
    f
    f
    ə
    a
    æ
    ɑː
    t
    d
    d
    ð


    e
    ə
    r
    r
    r
    r
    walkman
    L₁
    L₁
    L₁
    L₁
    L₁
    "All languages change, as long as they exist."
    (August Schleicher 1863)
    Comparative Linguistics
    2 / 45

    View full-size slide

  9. iPod
    Indo-European
    Germanic
    Old English
    English
    p
    f
    f
    f
    ə
    a
    æ
    ɑː
    t
    d
    d
    ð


    e
    ə
    r
    r
    r
    r
    walkman
    L₁
    L₁
    L₁
    L₁
    L₁
    "All languages change, as long as they exist."
    (August Schleicher 1863)
    Comparative Linguistics
    2 / 45

    View full-size slide

  10. iPod
    Indo-European
    Germanic
    Old English
    English
    p
    f
    f
    f
    ə
    a
    æ
    ɑː
    t
    d
    d
    ð


    e
    ə
    r
    r
    r
    r
    walkman
    L₁
    L₁
    L₁
    "All languages change, as long as they exist."
    (August Schleicher 1863)
    Comparative Linguistics
    2 / 45

    View full-size slide

  11. iPod
    Indo-European
    Germanic
    Old English
    English
    p
    f
    f
    f
    ə
    a
    æ
    ɑː
    t
    d
    d
    ð


    e
    ə
    r
    r
    r
    r
    walkman
    L₂
    L₁
    L₃
    "All languages change, as long as they exist."
    (August Schleicher 1863)
    Comparative Linguistics
    2 / 45

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  12. Comparative Linguistics Background
    Background
    3 / 45

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  13. Comparative Linguistics Background
    Background
    3 / 45

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  14. Comparative Linguistics Background
    Background
    3 / 45

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  15. Comparative Linguistics Background
    Background
    3 / 45

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  16. Comparative Linguistics Background
    Background
    3 / 45

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  17. Comparative Linguistics Comparative Method
    The Comparative Method
    COMPA-
    RATIVE
    METHOD
    4 / 45

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  18. Comparative Linguistics Comparative Method
    The Comparative Method
    COMPA-
    RATIVE
    METHOD
    4 / 45

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  19. Comparative Linguistics Comparative Method
    The Comparative Method
    COMPA-
    RATIVE
    METHOD
    4 / 45

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  20. Comparative Linguistics Comparative Method
    The Comparative Method
    COMPA-
    RATIVE
    METHOD
    4 / 45

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  21. Comparative Linguistics Comparative Method
    The Comparative Method
    COMPA-
    RATIVE
    METHOD
    4 / 45

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  22. Comparative Linguistics Computational Linguistics
    Computational Historical Linguistics
    COMPUTA-
    TIONAL
    HISTORICAL
    LINGUISTICS
    5 / 45

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  23. Comparative Linguistics Computational Linguistics
    Computational Historical Linguistics
    COMPUTA-
    TIONAL
    HISTORICAL
    LINGUISTICS
    5 / 45

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  24. Comparative Linguistics Computational Linguistics
    Computational Historical Linguistics
    COMPUTA-
    TIONAL
    HISTORICAL
    LINGUISTICS
    5 / 45

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  25. Comparative Linguistics Computational Linguistics
    Computational Historical Linguistics
    COMPUTA-
    TIONAL
    HISTORICAL
    LINGUISTICS
    5 / 45

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  26. Comparative Linguistics Computational Linguistics
    Computational Historical Linguistics
    COMPUTA-
    TIONAL
    HISTORICAL
    LINGUISTICS
    5 / 45

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  27. Comparative Linguistics Computational Linguistics
    Classical vs. Computational Language Comparison
    LC
    CA
    COMPA-
    RATIVE
    METHOD
    lacks
    efficiency
    lacks
    consistency
    lacks
    efficiency
    lacks
    accuracy
    lacks
    flexibility
    high
    efficiency
    high
    consistency
    high
    flexibility
    high
    accuracy
    COMPUTA-
    TIONAL
    HISTORICAL
    LINGUISTICS
    6 / 45

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  28. Comparative Linguistics Computational Linguistics
    Classical vs. Computational Language Comparison
    LC
    CA
    COMPA-
    RATIVE
    METHOD
    lacks
    efficiency
    lacks
    consistency
    lacks
    efficiency
    lacks
    accuracy
    lacks
    flexibility
    high
    efficiency
    high
    consistency
    high
    flexibility
    high
    accuracy
    COMPUTA-
    TIONAL
    HISTORICAL
    LINGUISTICS
    6 / 45

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  29. Comparative Linguistics Computational Linguistics
    Classical vs. Computational Language Comparison
    LC
    CA
    lacks
    efficiency
    lacks
    consistency
    lacks
    efficiency
    lacks
    accuracy
    lacks
    flexibility
    high
    efficiency
    high
    consistency
    high
    flexibility
    high
    accuracy
    COMPA-
    RATIVE
    METHOD accuracy
    flexibility
    consistency
    efficiency
    COMPUTA-
    TIONAL
    HISTORICAL
    LINGUISTICS
    6 / 45

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  30. Comparative Linguistics CALC
    Computer-Assisted Language Comparison LC
    CA
    LC
    CA
    lacks
    efficiency
    lacks
    consistency
    lacks
    efficiency
    lacks
    accuracy
    lacks
    flexibility
    high
    efficiency
    high
    consistency
    high
    flexibility
    high
    accuracy
    COMPA-
    RATIVE
    METHOD accuracy
    flexibility
    consistency
    efficiency
    COMPUTA-
    TIONAL
    HISTORICAL
    LINGUISTICS
    7 / 45

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  31. Comparative Linguistics CALC
    Computer-Assisted Language Comparison LC
    CA
    7 / 45

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  32. Historical Language Comparison
    8 / 45

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  33. Historical Language Comparison Sequences in Biology and Linguistics
    Alphabets in Biology and Linguistics
    9 / 45

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  34. Historical Language Comparison Sequences in Biology and Linguistics
    Alphabets in Biology and Linguistics
    • universal • language-specific
    9 / 45

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  35. Historical Language Comparison Sequences in Biology and Linguistics
    Alphabets in Biology and Linguistics
    • universal • language-specific
    • limited • widely varying
    9 / 45

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  36. Historical Language Comparison Sequences in Biology and Linguistics
    Alphabets in Biology and Linguistics
    • universal • language-specific
    • limited • widely varying
    • constant • mutable
    9 / 45

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  37. Historical Language Comparison Sound Change
    Sound Change
    Nase
    Nass
    Muse
    muss
    singen
    m → b
    n → d
    ng → g
    ss → th
    s → dh
    10 / 45

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  38. Historical Language Comparison Sound Change
    Sound Change
    Nase
    Nass
    Muse
    muss
    singen
    m → b
    n → d
    ng → g
    ss → th
    s → dh
    10 / 45

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  39. Historical Language Comparison Sound Change
    Sound Change
    Nase
    Nass
    Muse
    muss
    singen
    m → b
    n → d
    ng → g
    ss → th
    s → dh
    10 / 45

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  40. Historical Language Comparison Sound Change
    Sound Change
    bass
    buzic
    dose
    doizy
    sig
    math
    mudhic
    nothe
    noidhy
    thing
    10 / 45

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  41. Historical Language Comparison Sound Change
    Sound Change
    *mass
    *muzic
    *nose
    *noizy
    *sing
    bass
    buzic
    dose
    doizy
    sig
    math
    mudhic
    nothe
    noidhy
    thing
    10 / 45

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  42. Historical Language Comparison Sound Correspondences
    Inferring Correspondences
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    11 / 45

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  43. Historical Language Comparison Sound Correspondences
    Inferring Correspondences
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    11 / 45

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  44. Historical Language Comparison Sound Correspondences
    Inferring Correspondences
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    11 / 45

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  45. Historical Language Comparison Sound Correspondences
    Inferring Correspondences
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    11 / 45

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  46. Historical Language Comparison Sound Correspondences
    Inferring Correspondences
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    11 / 45

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  47. Historical Language Comparison Sound Correspondences
    Inferring Correspondences
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    11 / 45

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  48. Historical Language Comparison Sound Correspondences
    Inferring Correspondences
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    11 / 45

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  49. Historical Language Comparison Sound Correspondences
    Inferring Correspondences
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    11 / 45

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  50. Historical Language Comparison Homolog Detection
    Inferring Homologs
    Cognate List Alignment Correspondences
    Bola six kʰ j a u ʔ ⁵⁵ Bola Maru Freq.
    a(a̰) a(a̰) 3 x
    u u 3 x
    ʔ k 3 x
    j j 2 x
    k(ʰ) k(ʰ) 2 x
    ⁵⁵ ⁵⁵ 2 x
    ³¹ ³¹ 1 x
    Maru six kʰ j a u k ⁵⁵
    Bola lip k a̰ u ʔ ⁵⁵
    Maru lip k a̰ u k ⁵⁵
    Bola man j a u ʔ ³¹
    Maru man j a u k ³¹
    12 / 45

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  51. Historical Language Comparison Correspondence Patterns
    Inferring Patterns
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ

    tsʰ
    t


    13 / 45

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  52. Historical Language Comparison Correspondence Patterns
    Inferring Patterns
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ

    tsʰ
    t


    "salt"
    Bola tʰ a ³⁵
    Maru tsʰ ɔ ³⁵
    Rangoon sʰ ɑ ⁵⁵
    13 / 45

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  53. Historical Language Comparison Correspondence Patterns
    Inferring Patterns
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    t
    ts
    t


    13 / 45

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  54. Historical Language Comparison Correspondence Patterns
    Inferring Patterns
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    t
    ts
    t


    "tooth"
    Bola t u i ⁵⁵
    Maru ts ɔ i ³¹
    Rangoon tθ w a ⁵⁵
    13 / 45

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  55. Historical Language Comparison Correspondence Patterns
    Inferring Patterns
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    tʰ tʰ
    t


    13 / 45

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  56. Historical Language Comparison Correspondence Patterns
    Inferring Patterns
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    tʰ tʰ
    t


    "sharp"
    Bola tʰ a ʔ ⁵⁵
    Maru tʰ ɔ ʔ ⁵⁵
    Rangoon tʰ ɛ ʔ ⁴
    13 / 45

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  57. Historical Language Comparison Correspondence Patterns
    Inferring Patterns
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    t t
    t

    t
    13 / 45

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  58. Historical Language Comparison Correspondence Patterns
    Inferring Patterns
    Ø
    Sound Bola Maru Rangoon
    d Ø Ø d
    t t t t
    tʰ tʰ tʰ tʰ
    tθ Ø Ø tθ
    ts ts ts Ø
    tsʰ tsʰ tsʰ Ø
    tʃ tʃ tʃ Ø
    tʃʰ tʃʰ tʃʰ Ø
    s s s s
    sʰ Ø Ø sʰ
    ɕ Ø Ø ɕ
    ʃ ʃ ʃ
    t t
    t

    t
    "wing"
    Bola t a u ŋ ⁵⁵
    Maru t a u ŋ ³¹
    Rangoon t ɑ u ∼ ²²
    13 / 45

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  59. Inferring Correspondence Patterns
    14 / 45

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  60. Inferring Correspondence Patterns From Sound Correspondences to Correspondence Patterns
    Sound Correspondences
    Gloss Proto-Germanic German English Dutch
    ‘dead’ *daudaz daudaz tot toːt dead dɛd doot doːt
    ‘deed’ *dēdiz deːdiz Tat taːt deed diːd daad daːt
    ‘thick’ *þekuz θekuz dick dɪk thick θɪk dik dɪk
    ‘thorn’ *þurnuz θurnuz Dorn dɔrn thorn θɔːn doorn doːrn
    ‘tongue’ *tungōn tuŋgoːn Zunge tsʊŋə tongue tʌŋ tong tɔŋ
    ‘tooth’ *tanþs tanθs Zahn tsaːn tooth tuːθ tand tɑnt
    15 / 45

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  61. Inferring Correspondence Patterns From Sound Correspondences to Correspondence Patterns
    Sound Correspondence Patterns
    PIE Hittite Sanskrit Avestan Greek Latin Gothic
    Old
    Church
    Slavonic
    Lithuanian Old Irish Armenian Tocharian
    *p p p p f p p f b p p Ø h w Ø p
    *b b p b bβ b b p b b b p p
    *bʰ b p bʱ/bh bβ pʰ/ph f b b b b b b p
    *t t t t θ t t θ/þ d t t t tʼ j/y t tʃ/c
    *d d t d d ð d d t d d d t ts ʃ/ś
    *dʰ d t dʰ/dh h d ð tʰ/th f d b d d d d t t tʃ/c
    ... ... ... ... ... ... ... ... ... ... ... ...
    *kʷ kʷ/ku k c k c k p t kʷ/qu hʷ/hw g k tʃ/č k c kʼ tʃʼ/čʼ k ʃʲ/ś
    *gʷ kʷ/u g j g j g b d gʷ/gu u q g ʒ/ž z g b k k ś
    *gʷʰ kʷ/ku gʷ/gu gʱ/gh h g j pʰ/ph tʰ/th kʰ/kh f gʷ/gu u g b g ʒ/ž z g g g dʒ/ǰ k ʃʲ/ś
    Clackson (2007: 37)
    16 / 45

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  62. Inferring Correspondence Patterns From Sound Correspondences to Correspondence Patterns
    Sound Correspondence Patterns and Alignments
    d au d ( a z )
    t oː t ( - - )
    d ɛ d ( - - )
    d oː t ( - - )
    d eː d ( i z )
    t aː t ( - - )
    d iː d ( - - )
    d a: t ( - - )
    θ e k ( u z )
    d ɪ k ( - - )
    θ ɪ k ( - - )
    d ɪ k ( - - )
    θ u r n ( u z )
    d ɔ r n ( - - )
    θ ɔː - n ( - - )
    d oː r n ( - - )
    t u ŋ ( g oː )
    ts ʊ ŋ ( - ə )
    t ʌ ŋ ( - - )
    t ɔ ŋ ( - - )
    t a n θ ( s )
    ts aː n - ( - )
    t uː - θ ( - )
    t ɑ n t ( - )
    Proto-Germanic
    German
    English
    Dutch
    Proto-Germanic
    German
    English
    Dutch
    'dead' 'thick' 'tongue'
    'deed' 'thorn' 'tooth'
    17 / 45

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  63. Inferring Correspondence Patterns From Sound Correspondences to Correspondence Patterns
    Sound Correspondence Patterns and Alignments
    A B C D E F
    Sanskrit y u g a m dh u h i (tar) s n u ṣ (ā) - r u dh (iras)
    Greek z u g o n th u g a (ter-) - n u - (os) e r u th (rós)
    Latin i u g u m Ø Ø Ø Ø (Ø) - n u r (us) - r u b (er)
    Gothic j u k - - d au h - (tar) Ø Ø Ø Ø (Ø) Ø Ø Ø Ø (Ø)
    Gloss 'yoke' 'daughter' 'daughter-in-law' 'red'
    Adapted from Anttila (1972)
    18 / 45

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  64. Inferring Correspondence Patterns From Sound Correspondences to Correspondence Patterns
    Summary on Sound Correspondence Patterns
    19 / 45

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  65. Inferring Correspondence Patterns From Sound Correspondences to Correspondence Patterns
    Summary on Sound Correspondence Patterns
    correspondence patterns in linguistics are a way to encode mappings
    across several different alphabets
    19 / 45

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  66. Inferring Correspondence Patterns From Sound Correspondences to Correspondence Patterns
    Summary on Sound Correspondence Patterns
    correspondence patterns in linguistics are a way to encode mappings
    across several different alphabets
    they are usually inferred manually, by inspecting “correspondence
    sets” (Clackson 2007: 29f) of words (i.e., cognate sets with recurring
    sounds)
    19 / 45

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  67. Inferring Correspondence Patterns From Sound Correspondences to Correspondence Patterns
    Summary on Sound Correspondence Patterns
    correspondence patterns in linguistics are a way to encode mappings
    across several different alphabets
    they are usually inferred manually, by inspecting “correspondence
    sets” (Clackson 2007: 29f) of words (i.e., cognate sets with recurring
    sounds)
    the main problem of correspondence pattern identification is the
    handling of missing data, since not all cognate sets will necessarily
    contain reflexes from each of the languages under investigation
    19 / 45

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  68. Inferring Correspondence Patterns From Sound Correspondences to Correspondence Patterns
    Summary on Sound Correspondence Patterns
    θ u r n ( u z )
    d ɔ r n ( - - )
    θ ɔː - n ( - - )
    d oː r n ( - - )
    'thorn'
    alignment
    site
    sound
    correspondence
    pattern
    θ e k ( u z )
    d ɪ k ( - - )
    θ ɪ k ( - - )
    d ɪ k ( - - )
    'thick'
    Proto-Germanic
    German
    English
    Dutch
    θ
    d
    θ
    d
    θ u r p ( a )
    Ø Ø Ø Ø Ø Ø Ø
    d ɔ r f ( - )
    d ɔ r p ( - )
    'thorp'
    20 / 45

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  69. Inferring Correspondence Patterns Preliminaries on Correspondence Pattern Recognition
    Compatibility of Alignment Sites
    A E A F E F A C C E C F
    Sanskrit u <=> u ------ u <=> u ------ u <=> u ------ u <=> u ------ u <=> u ------ u <=> u
    Greek u <=> u u <=> u u <=> u u <=> u u <=> u u <=> u
    Latin u <=> u u <=> u u <=> u u ? Ø Ø ? u Ø ? u
    Gothic u ? Ø u ? Ø Ø ? Ø u >=< au au ? Ø au ? Ø
    Matches 3 3 3 2 2 2
    21 / 45

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  70. Inferring Correspondence Patterns Preliminaries on Correspondence Pattern Recognition
    Compatibility of Alignment Sites
    Two alignment sites are assumed to be compatible, if they
    (a) share at least one sound,
    (b) do not have any conflicting sounds.
    22 / 45

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  71. Inferring Correspondence Patterns Preliminaries on Correspondence Pattern Recognition
    Compabitility of Alignment Sites
    Cognate Set L1 L2 L3 L4 L5 L6 L7 L8
    “hand-1” p p p Ø f f Ø p
    “foot-1” p p p p f f p p
    ⊠ compatible
    □ incompatible
    23 / 45

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  72. Inferring Correspondence Patterns Preliminaries on Correspondence Pattern Recognition
    Compabitility of Alignment Sites
    Cognate Set L1 L2 L3 L4 L5 L6 L7 L8
    “hand-1” p p p Ø f f Ø p
    “foot-1” p p p p f f p p
    ⊠ compatible
    □ incompatible
    Cognate Set L1 L2 L3 L4 L5 L6 L7 L8
    “hand-1” p p p Ø f f Ø p
    “leg-1” p p f pf f f p p
    □ compatible
    ⊠ incompatible
    23 / 45

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  73. Inferring Correspondence Patterns Preliminaries on Correspondence Pattern Recognition
    Alignment Site Networks
    Constructing an alignment site network from a set of alignments:
    all sites represent a node in the network
    edges are drawn between compatible sites
    edges can (in principle) also be weighted by the number of matching
    sounds (but disregarded in our algorithm so far)
    24 / 45

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  74. Inferring Correspondence Patterns Preliminaries on Correspondence Pattern Recognition
    Alignment Site Networks
    Sanskrit
    Greek
    Latin
    Gothic
    E
    u
    u
    u
    Ø
    Sanskrit
    Greek
    Latin
    Gothic
    F
    u
    u
    u
    Ø
    A
    u
    u
    u
    Sanskrit
    Greek
    Latin
    Gothic u
    Sanskrit
    Greek
    Latin
    Gothic
    C
    u
    u
    Ø
    au
    25 / 45

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  75. Inferring Correspondence Patterns Preliminaries on Correspondence Pattern Recognition
    Correspondence Pattern Inference as Clique Cover Problem
    26 / 45

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  76. Inferring Correspondence Patterns Preliminaries on Correspondence Pattern Recognition
    Correspondence Pattern Inference as Clique Cover Problem
    The clique cover problem (also called clique partitioning problem, see
    Bhasker 1991) is the inverse of the famous graph coloring problem
    and has been shown to be NP-hard.
    26 / 45

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  77. Inferring Correspondence Patterns Preliminaries on Correspondence Pattern Recognition
    Correspondence Pattern Inference as Clique Cover Problem
    The clique cover problem (also called clique partitioning problem, see
    Bhasker 1991) is the inverse of the famous graph coloring problem
    and has been shown to be NP-hard.
    The goal of the problem is to split a graph into the smallest number
    of cliques in which each node is represented by exactly one clique.
    26 / 45

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  78. Inferring Correspondence Patterns Preliminaries on Correspondence Pattern Recognition
    Correspondence Pattern Inference as Clique Cover Problem
    The clique cover problem (also called clique partitioning problem, see
    Bhasker 1991) is the inverse of the famous graph coloring problem
    and has been shown to be NP-hard.
    The goal of the problem is to split a graph into the smallest number
    of cliques in which each node is represented by exactly one clique.
    We assume (but we cannot formally prove it) that the clique cover of
    our graph of compatible correspondence sets will come close to the
    best set of sound correspondence patterns in our data.
    26 / 45

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  79. Inferring Correspondence Patterns Preliminaries on Correspondence Pattern Recognition
    Correspondence Pattern Inference as Clique Cover Problem
    The clique cover problem (also called clique partitioning problem, see
    Bhasker 1991) is the inverse of the famous graph coloring problem
    and has been shown to be NP-hard.
    The goal of the problem is to split a graph into the smallest number
    of cliques in which each node is represented by exactly one clique.
    We assume (but we cannot formally prove it) that the clique cover of
    our graph of compatible correspondence sets will come close to the
    best set of sound correspondence patterns in our data.
    Partitioning our alignment site network into cliques does not solve the
    problem of linguistic reconstruction, but it can be seen as its
    fundamental prerequisite.
    26 / 45

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  80. A Method for Correspondence Pattern Recognition Description of the Method
    General Workflow
    word list
    A
    B
    cognates
    A
    B
    alignments
    site network
    1 2
    clique coverage
    1
    2
    fuzzy sites
    (A) (B)
    (C) (D) (E)
    27 / 45

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  81. A Method for Correspondence Pattern Recognition Description of the Method
    Workflow Example
    cc-figure.pdf
    28 / 45

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  82. A Method for Correspondence Pattern Recognition Description of the Method
    Implementation, Input, Output
    Full implementation is provided as plugin for LingPy
    (http://lingpy.org).
    An approximate version is shipped with the EDICTOR
    (http://edictor.digling.org).
    Input format follows the format employed by LingPy with some
    additional required columns which need to be submitted.
    Output in form of annotated word lists with assigned patterns for
    each word form, which can be read in and inspected with help of the
    EDICTOR, or in form of “normal” text files.
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  83. A Method for Correspondence Pattern Recognition Description of the Method
    Implementation, Input, Output
    ID DOCULECT CONCEPT FORM TOKENS STRUCTURE COGID ALIGNMENT
    1 German tongue Zunge ts ʊ ŋ ə c v c 1 ts ʊ ŋ ( ə )
    2 English tongue tongue t ʌ ŋ c v c 1 t ʌ ŋ ( - )
    3 Dutch tongue tong t ɔ ŋ c v c 1 t ɔ ŋ ( - )
    4 German tooth Zahn ts aː n c v c 2 ts aː n -
    5 English tooth tooth t uː θ c v c 2 t uː - θ
    6 Dutch tooth tand t ɑ n t c v c 2 t ɑ n t
    7 German thick dick d ɪ k c v c 3 d ɪ k
    ... ... ... ... ... ... ... ...
    30 / 45

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  84. A Method for Correspondence Pattern Recognition Description of the Method
    Correspondence Pattern Recognition
    A three-step algorithm:
    (A) sort the alignment sites with a customized variant of the Quicksort
    algorithm (Hoare 1962) which groups compatible alignment sites
    closely together (sole algorithm used by EDICTOR due to restrictions
    of JavaScript on memory)
    (B) inverse version of Welsh-Powell algorithm (Welsh and Powell 1967)
    for graph coloring
    (a) sort all partitions according to size and alignment site density
    (b) pick first partition and compare it against all other partitions, merge
    with compatible partitions, put incompatible partitions into a queue
    (c) finish if no more partitions are in the queue
    (C) compare all alignment sites again with the inferred correspondence
    patterns and assign each alignment site to all patterns with which it is
    compatible to yield a fuzzy clustering
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  85. A Method for Correspondence Pattern Recognition Description of the Method
    Correspondence Pattern Recognition
    L₁ L₂ L₃ L₄
    S₁ k Ø Ø k
    S₂ k g Ø k
    S₃ Ø g g k
    L₁ L₂ L₃ L₄
    S₁ 1 0 0 1
    S₂ 1 1 0 1
    S₃ 0 1 1 1
    2
    8 / (4 · 3) = 0.66
    3
    3
    }
    calculating alignment site density
    32 / 45

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  86. A Method for Correspondence Pattern Recognition Testing
    Test and Training Data
    Dataset Source Languages Concepts Cognates Density
    Austronesian Greenhill et al. (2008) 20 210 2864 0.34
    Bai Wang (2006) 9 110 285 0.73
    Chinese Hóu (2004) 15 140 1189 0.60
    IndoEuropean Dunn (2012) 20 207 1777 0.60
    Japanese Hattori (1973) 10 200 460 0.70
    ObUgrian Zhivlov (2011) 21 110 242 0.88
    Bahnaric Sidwell (2015) 24 200 1055 0.76
    Chinese Běijīng Dàxué (1964) 18 180 1231 0.68
    Huon McElhanon (1967) 14 139 855 0.48
    Romance Saenko (2015) 43 110 465 0.90
    Tujia Starostin (2013) 5 109 179 0.63
    Uralic Syrjänen et al. (2013) 7 173 870 0.39
    test and training data (List et al. 2017)
    33 / 45

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  87. A Method for Correspondence Pattern Recognition Testing
    Test and Training Data
    D = 1 −
    1
    m
    m

    i=1
    1
    ni
    ni

    j=1
    1
    cognates(wij)
    (1)
    calculating the cognate density for a given wordlist
    34 / 45

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  88. A Method for Correspondence Pattern Recognition Testing
    General Characteristics
    All Patterns Consonants Vowels
    Dataset St. Pt. Sg. Fz. St. Pt. Sg. Fz. St. Pt. Sg. Fz.
    Bahnaric 2659 865 385 4.59 1651 480 222 4.53 1008 382 167 4.52
    Chinese 3205 584 191 5.78 1118 207 79 3.81 1308 298 108 7.28
    Huon 1572 271 104 4.07 873 154 58 2.98 699 115 40 5.42
    Romance 1656 874 587 3.67 940 496 345 3.51 716 379 250 3.85
    Tujia 952 272 130 2.66 323 118 62 1.71 347 84 41 2.71
    Uralic 1346 326 131 3.35 763 180 74 2.75 583 141 45 4.16
    St: Alignment Sites, Pt: Correspondence Patterns, Sg: Singleton Patterns,
    Fz: Fuzziness of alignment sites
    35 / 45

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  89. A Method for Correspondence Pattern Recognition Testing
    Specific Characteristics
    seeding of artificial borrowings or wrong cognates as a method for testing
    following Dessimoz et al. (2008), for a biological framework,
    randomly select pairs of languages and have them interchange words
    building on Dessimoz et al. (2008), create neologisms with LingPy’s
    built-in word generator (based on Markov Chains), to replace existing
    words in a given cognate set with a new (possible) word from the
    same language
    investigate the pattern regularity (PR) of the cognate sets in the data
    before and after the operations by
    (a) setting a user-defined threshold for the regularity of an alignment site
    derived from the density of its pattern (smoothing of singletons: they
    are always irregular)
    (b) accepting a cognate set as regular if half of its alignment sites are
    regular
    (c) splitting irregular cognate sets up into independent cognate sets
    36 / 45

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  90. A Method for Correspondence Pattern Recognition Testing
    Specific Characteristics: Fake Borrowings
    Unmodified Modified Diff.
    Dataset Orig. D. PR D. Orig. D. PR D. Lg. Ev.
    Bahnaric 0.76 0.51 0.76 0.45 0.06 4 400
    Chinese 0.68 0.55 0.68 0.50 0.05 3 270
    Huon 0.48 0.19 0.48 0.22 -0.03 2 139
    Romance 0.90 0.38 0.90 0.24 0.14 8 440
    Tujia 0.63 0.59 0.63 0.55 0.04 1 54
    Uralic 0.39 0.38 0.39 0.37 0.01 1 86
    Orig Ds: original density, PR Ds: density after applying PR check, Lg:
    number of languages selected, Diff: difference between original and modified
    density, Ev: borrowing events
    37 / 45

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  91. A Method for Correspondence Pattern Recognition Testing
    Specific Characteristics: Fake Neologisms
    Unmodified Modified Diff. Lg. Ev.
    Dataset Orig. Ds. PR Ds. Orig. Ds. PR Ds.
    Bahnaric 0.76 0.51 0.77 0.48 0.03 288 400
    Chinese 0.68 0.55 0.69 0.47 0.09 162 270
    Huon 0.48 0.19 0.50 0.17 0.01 98 139
    Romance 0.90 0.38 0.91 0.32 0.07 924 440
    Tujia 0.63 0.59 0.64 0.58 0.02 12 54
    Uralic 0.39 0.38 0.41 0.40 -0.02 24 86
    Orig Ds: original density, PR Ds: density after applying PR check, Lg:
    number of language pairs (donor-recipient), Diff: difference between original
    and modified density, Ev: borrowing events
    38 / 45

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  92. A Method for Correspondence Pattern Recognition Testing
    Specific Characteristics: Summary
    the fake borrowings lead as expected to a decrease in cognate density
    the fake neologisms also lead as expected to a decrease in cognate
    density
    even pulling out those correspondence patterns which are singletons
    or marking the cognate sets which have a low density seems like a
    valuable enterprise as it can help linguists to have another look at
    their data and check the findings manually
    39 / 45

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  93. A Method for Correspondence Pattern Recognition Examples
    Examples: Burmish Graph with Clique Cover (with N. Hill)
    tʃʰ
    s ʃ tʃʰ x
    j
    n◌̥
    n
    n
    ŋ
    tsʰ
    tsʰ
    tsʰ
    tʃʰ


    pʰ j
    w
    x
    v
    x
    v
    x
    j
    x
    n
    n
    x
    x
    x
    n◌̥
    x
    n
    m
    m
    m
    n
    kʰ kʰ









    -
    -
    -


    p
    j
    ɣ
    ɣ
    ɣ
    ɣ
    j
    j
    j
    n
    n
    n
    n
    m
    n
    n
    n
    n
    m
    m
    m
    m
    m
    m
    m
    m
    m
    m
    l
    l
    l
    p
    l
    l
    l
    l
    l
    l
    l










    ʃ
    s
    s
    s
    k
    ʃ
    ʃ
    k
    s
    s
    k s
    ʃ
    ʃ
    ʃ
    t
    t
    t
    t
    t
    t
    t
    t
    t
    t
    t
    m
    m
    m
    m m
    m
    m
    m
    m
    p
    p
    p
    p
    p
    p
    p
    p
    p
    p
    p
    p
    p
    p
    p
    k
    k s kʰ
    kʰ k
    ʃ
    ŋ n m l t t l -
    tsʰ f tʃ k n n
    l
    ʃ
    tsʰ
    l
    s
    m
    t p
    k
    n
    kʰ m j
    m
    v j
    s

    n
    ts
    m
    l ŋ
    k kʰ
    v ʃ
    ʐ ʃ n
    k
    j -
    tʃ pʰ
    s
    v m
    k ŋ ŋ - n n l
    n◌̥
    ŋ
    ŋ
    l
    l
    l
    l
    ʃ
    ʃ
    ts
    ʃ k
    s
    k
    s
    s
    s
    s
    s
    ts
    ts
    ts
    ts
    ts
    ts
    j k
    j
    ɣ
    k
    ŋ
    ŋ
    ŋ
    ŋ
    ŋ
    ŋ
    ŋ
    s
    s







    k
    k
    k
    k
    k
    ts p

    ts

    ŋ
    n ŋ
    k
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  94. A Method for Correspondence Pattern Recognition Examples
    Examples: Burmish Graph with Clique Cover (with N. Hill)











    ŋ
    ŋ
    ŋ
    ŋ
    ŋ
    ŋ
    ŋ
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    tsʰ
    tsʰ
    tʃʰ
    tsʰ
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    tʃʰ
    tʃʰ
    tsʰ
    j v f j
    v v
    ŋ
    -
    ŋ
    n◌̥
    ŋ
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    ŋ
    ʃ
    ʃ
    s
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    ʃ
    ʃ
    s





    s




    x
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    ʃ ʃ
    ʃ
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    ʃ
    ʃ
    ts
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    p
    p
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    m
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    s s
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    s
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    l
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    l
    s
    s
    s
    s
    s
    s
    p
    p
    p
    p
    p
    p
    p
    p
    p
    p
    p
    p
    p
    p
    p

    p
    m
    m
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    m
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    m
    m
    l
    l
    l
    l
    l
    l
    l
    l
    -
    -
    j
    j
    j
    j
    j
    -
    j
    k
    ɣ
    ɣ
    ɣ
    ɣ
    ʐ
    ɣ
    w
    j
    -
    v
    v
    j
    j
    j
    k
    k k k
    k
    k
    k
    k
    k







    k
    k
    k
    k
    k











    x
    x
    x
    x
    n
    n◌̥
    n
    n
    n
    n
    n
    ŋ
    n
    n n
    n
    n
    n
    n
    n
    n
    n
    n
    n
    k k
    k
    k
    k k
    m
    m
    m
    m
    m
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    m
    m
    -
    n
    n
    ŋ
    ŋ
    n
    n◌̥
    n

    m
    m
    m
    m
    m
    m
    m
    m
    m
    40 / 45

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  95. A Method for Correspondence Pattern Recognition Examples
    Examples: Burmish Graph with Clique Cover (with N. Hill)
    tʃʰ
    tʃʰ
    tsʰ
    tsʰ
    tsʰ
    tsʰ


    tsʰ
    tʃʰ





    40 / 45

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  96. A Method for Correspondence Pattern Recognition Examples
    Examples: Burmish Graph with Clique Cover (with N. Hill)
    tʃʰ
    tʃʰ
    tʃʰ
    tsʰ
    tsʰ
    tsʰ
    tsʰ
    tsʰ







    Clique Cogn. Concept Achang Atsi Bola Lashi Maru Old B. Rang. Xiand.
    41 659 "goat" Ø Ø Ø Ø tʃʰ tsʰ sʰ Ø
    41 672 "armpit" Ø Ø tʃʰ tʃʰ tʃʰ Ø Ø Ø
    41 433 "rice" tsʰ tʃʰ tʃʰ tʃʰ tʃʰ Ø sʰ tsʰ
    Clique Cogn. Concept Achang Atsi Bola Lashi Maru Old B. Rang. Xiand.
    74 55 "ten" Ø Ø tʰ tsʰ Ø Ø Ø tsʰ
    42 53 "ten" tɕʰ tsʰ Ø Ø Ø tsʰ sʰ Ø
    42 421 "salt" tɕʰ tsʰ tʰ tsʰ tsʰ tsʰ sʰ cʰ
    42 129 "twenty" Ø tsʰ tʰ tsʰ tsʰ tsʰ sʰ Ø
    83 287 "hair" Ø tsʰ tsʰ tsʰ tsʰ tsʰ sʰ Ø
    Clique Cogn. Concept Achang Atsi Bola Lashi Maru Old B. Rang. Xiand.
    17 639 "above" Ø tʰ tʰ Ø tʰ tʰ tʰ Ø
    17 472 "sing" Ø tʰ tʰ Ø tʰ Ø Ø Ø
    17 61 "that" tʰ Ø Ø tʰ tʰ tʰ tʰ tʰ
    17 323 "sharp" tʰ tʰ tʰ tʰ tʰ tʰ tʰ Ø
    17 66 "there" tʰ Ø tʰ tʰ tʰ Ø tʰ tʰ
    17 547 "firewood" tʰ tʰ tʰ tʰ tʰ tʰ tʰ tʰ
    17 74 "thick" Ø tʰ tʰ tʰ tʰ Ø tʰ Ø
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  97. A Method for Correspondence Pattern Recognition Examples
    Examples: Burmish Graph with Clique Cover (with N. Hill)
    Clique Cogn. Concept Achang Atsi Bola Lashi Maru Old B. Rang. Xiand.
    41 659 "goat" Ø Ø Ø Ø tʃʰ tsʰ sʰ Ø
    41 672 "armpit" Ø Ø tʃʰ tʃʰ tʃʰ Ø Ø Ø
    41 433 "rice" tsʰ tʃʰ tʃʰ tʃʰ tʃʰ Ø sʰ tsʰ
    Clique Cogn. Concept Achang Atsi Bola Lashi Maru Old B. Rang. Xiand.
    74 55 "ten" Ø Ø tʰ tsʰ Ø Ø Ø tsʰ
    42 53 "ten" tɕʰ tsʰ Ø Ø Ø tsʰ sʰ Ø
    42 421 "salt" tɕʰ tsʰ tʰ tsʰ tsʰ tsʰ sʰ cʰ
    42 129 "twenty" Ø tsʰ tʰ tsʰ tsʰ tsʰ sʰ Ø
    83 287 "hair" Ø tsʰ tsʰ tsʰ tsʰ tsʰ sʰ Ø
    Clique Cogn. Concept Achang Atsi Bola Lashi Maru Old B. Rang. Xiand.
    17 639 "above" Ø tʰ tʰ Ø tʰ tʰ tʰ Ø
    17 472 "sing" Ø tʰ tʰ Ø tʰ Ø Ø Ø
    17 61 "that" tʰ Ø Ø tʰ tʰ tʰ tʰ tʰ
    17 323 "sharp" tʰ tʰ tʰ tʰ tʰ tʰ tʰ Ø
    17 66 "there" tʰ Ø tʰ tʰ tʰ Ø tʰ tʰ
    17 547 "firewood" tʰ tʰ tʰ tʰ tʰ tʰ tʰ tʰ
    17 74 "thick" Ø tʰ tʰ tʰ tʰ Ø tʰ Ø
    tʃʰ
    tʃʰ
    tʃʰ
    tsʰ
    tsʰ
    tsʰ
    tsʰ
    tsʰ







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  98. A Method for Correspondence Pattern Recognition Examples
    Examples: EDICTOR and Software Demo
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  99. Outlook
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  100. A Method for Correspondence Pattern Recognition Examples
    Outlook
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  101. A Method for Correspondence Pattern Recognition Examples
    Outlook
    the proposed inference of correspondence patterns is a first attempt
    to account for systemic aspects of sound change in a rigorous manner
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  102. A Method for Correspondence Pattern Recognition Examples
    Outlook
    the proposed inference of correspondence patterns is a first attempt
    to account for systemic aspects of sound change in a rigorous manner
    in contrast to many approaches proposed so far, it does not require
    family trees in any form, networks are just enough, but the patterns
    inferred can be used to study tree-like aspects of evolution (Chacon
    and List 2015),
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  103. A Method for Correspondence Pattern Recognition Examples
    Outlook
    the proposed inference of correspondence patterns is a first attempt
    to account for systemic aspects of sound change in a rigorous manner
    in contrast to many approaches proposed so far, it does not require
    family trees in any form, networks are just enough, but the patterns
    inferred can be used to study tree-like aspects of evolution (Chacon
    and List 2015),
    the algorithm needs to be further tested
    we need a deeper discussion in the field about the importance of
    correspondence patterns for linguistic reconstruction
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  104. A Method for Correspondence Pattern Recognition Examples
    Acknowledgements
    Nathan W. Hill (essential discussions on the implications of the
    procedure and further applications, intensive manual inspection of the
    output of the method)
    Taraka Rama (testing the method for alignment-based phylogenetic
    tree reconstruction, comments on draft and code)
    Eric Bapteste, Philippe Lopez, and their Team AIRE (providing initial
    inspiration and follow-up discussions on the approach, thanks to a
    similar approach applied in biology)
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  105. Thanks you for listening!
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