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Beyond Cognacy

Beyond Cognacy

Talk presented at the workshop "Towards a Global Language Phylogeny", 17-19 September, Max Planck Institute for History and the Sciences, Jena.

Johann-Mattis List

September 17, 2014
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  1. Beyond Cognacy
    Current Chances and Future Challenges of Automatic Cognate
    Detection in Historical Linguistics
    Johann-Mattis List
    Forschungszentrum Deutscher Sprachatlas
    Philipps-University Marburg
    2014-09-17
    1 / 30

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  2. word Wort слово
    cuvînt palabra
    mot adottszó slovo verbum
    focal 词 parola λόγος
    शब◌्
    द ord
    λόγος Wort слово
    cuvînt palabra
    mot adottszó slovo verbum
    focal 词 parola
    शब◌्
    द ord
    word
    ord
    ord
    word
    Cognate Detection
    2 / 30

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  3. Cognate Detection Traditional Approaches
    Traditional Approaches
    FRANZ BOPP
    VERY,
    VERY
    LONG
    TITLE
    3 / 30

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  4. Cognate Detection Traditional Approaches
    The Comparative Method
    FRANZ BOPP
    VERY,
    VERY
    LONG
    TITLE
    proof of
    relationship
    identification
    of cognates
    identification of
    sound correspondences
    reconstruction
    of proto-forms
    internal
    classification
    4 / 30

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  5. Cognate Detection Traditional Approaches
    The Comparative Method
    FRANZ BOPP
    VERY,
    VERY
    LONG
    TITLE
    proof of
    relationship
    identification
    of cognates
    identification of
    sound correspondences
    reconstruction
    of proto-forms
    internal
    classification
    4 / 30

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  6. Cognate Detection Traditional Approaches
    Cognate Detection
    FRANZ BOPP
    VERY,
    VERY
    LONG
    TITLE
    5 / 30

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  7. Cognate Detection Traditional Approaches
    Cognate Detection
    FRANZ BOPP
    VERY,
    VERY
    LONG
    TITLE
    Cognate List Alignment Correspondence List
    German dünn d ʏ n GER ENG Frequ.
    d θ 3 x
    d d 1 x
    n n 1 x
    m m 1 x
    ŋ ŋ 1 x
    English thin θ ɪ n
    German Ding d ɪ ŋ
    English thing θ ɪ ŋ
    German dumm d ʊ m
    English dumb d ʌ m
    German Dorn d ɔɐ n
    English thorn d ɔː n
    5 / 30

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  8. Cognate Detection Traditional Approaches
    Cognate Detection
    FRANZ BOPP
    VERY,
    VERY
    LONG
    TITLE
    Cognate List Alignment Correspondence List
    German dünn d ʏ n GER ENG Frequ.
    d θ 3 x
    d d 1 x
    n n 1 x
    m m 1 x
    ŋ ŋ 1 x
    English thin θ ɪ n
    German Ding d ɪ ŋ
    English thing θ ɪ ŋ
    German dumm d ʊ m
    English dumb d ʌ m
    German Dorn d ɔɐ n
    English thorn d ɔː n
    5 / 30

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  9. Cognate Detection Traditional Approaches
    Cognate Detection
    FRANZ BOPP
    VERY,
    VERY
    LONG
    TITLE
    Cognate List Alignment Correspondence List
    German dünn d ʏ n GER ENG Frequ.
    d θ 2 x
    d d 1 x
    n n 1 x
    m m 1 x
    ŋ ŋ 1 x
    English thin θ ɪ n
    German Ding d ɪ ŋ
    English thing θ ɪ ŋ
    German dumm d ʊ m
    English dumb d ʌ m
    German Dorn d ɔɐ n
    English thorn d ɔː n
    5 / 30

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  10. Cognate Detection Traditional Approaches
    Cognate Detection
    FRANZ BOPP
    VERY,
    VERY
    LONG
    TITLE
    Cognate List Alignment Correspondence List
    German dünn d ʏ n GER ENG Frequ.
    d θ 2 x
    d d 1 x
    n n 1 x
    m m 1 x
    ŋ ŋ 1 x
    English thin θ ɪ n
    German Ding d ɪ ŋ
    English thing θ ɪ ŋ
    German dumm d ʊ m
    English dumb d ʌ m
    German Dorn d ɔɐ n
    English thorn θ ɔː n
    5 / 30

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  11. Cognate Detection Traditional Approaches
    Cognate Detection
    FRANZ BOPP
    VERY,
    VERY
    LONG
    TITLE
    Cognate List Alignment Correspondence List
    German dünn d ʏ n GER ENG Frequ.
    d θ 3 x
    d d 1 x ?
    n n 2 x
    m m 1 x
    ŋ ŋ 1 x
    English thin θ ɪ n
    German Ding d ɪ ŋ
    English thing θ ɪ ŋ
    German dumm d ʊ m
    English dumb d ʌ m
    German Dorn d ɔɐ n
    English thorn θ ɔː n
    5 / 30

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  12. Cognate Detection Traditional Approaches
    Cognate Detection
    FRANZ BOPP
    VERY,
    VERY
    LONG
    TITLE
    Cognate List Alignment Correspondence List
    German dünn d ʏ n GER ENG Frequ.
    d θ 3 x
    d d 1 x
    n n 2 x
    m m 1 x
    ŋ ŋ 1 x
    English thin θ ɪ n
    German Ding d ɪ ŋ
    English thing θ ɪ ŋ
    German dumm d ʊ m
    English dumb d ʌ m
    German Dorn d ɔɐ n
    English thorn θ ɔː n
    5 / 30

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  13. Cognate Detection Automatic Approaches
    Automatic Approaches
    P(A|B)=(P(B|A)P(A))/(P(B)
    6 / 30

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  14. Cognate Detection Automatic Approaches
    Narrowing down the Task
    P(A|B)=(P(B|A)P(A))/(P(B)
    7 / 30

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  15. Cognate Detection Automatic Approaches
    Narrowing down the Task
    P(A|B)=(P(B|A)P(A))/(P(B)
    Traditional Workflow
    *dent-
    dente
    dɑ̃
    dɛnte
    *tanθ
    tuːθ
    t͡saːn
    DICTIONARIES
    WORDLISTS
    HISTORICAL SCENARIOS
    7 / 30

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  16. Cognate Detection Automatic Approaches
    Narrowing down the Task
    P(A|B)=(P(B|A)P(A))/(P(B)
    Traditional Workflow
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    *dent-
    dente
    dɑ̃
    dɛnte
    *tanθ
    tuːθ
    t͡saːn
    DICTIONARIES
    WORDLISTS
    HISTORICAL SCENARIOS
    7 / 30

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  17. Cognate Detection Automatic Approaches
    Narrowing down the Task
    P(A|B)=(P(B|A)P(A))/(P(B)
    Traditional Workflow
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    *dent-
    dente
    dɑ̃
    dɛnte
    *tanθ
    tuːθ
    t͡saːn
    DICTIONARIES
    WORDLISTS
    HISTORICAL SCENARIOS
    7 / 30

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  18. Cognate Detection Automatic Approaches
    Narrowing down the Task
    P(A|B)=(P(B|A)P(A))/(P(B)
    Technical Workflow
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    WORDLIST
    DATA
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    RAW
    DATA
    Semantic
    Tagging
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    TOKENS,
    MORPHEMES
    Tokenization
    Cognate
    Detection
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    COGNATE
    SETS
    Alignment
    Analysis
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    SOUND
    CORRESPON-
    DENCES
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    PROTO-
    FORMS
    Linguistic
    Reconstruction
    7 / 30

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  19. Cognate Detection Automatic Approaches
    Narrowing down the Task
    P(A|B)=(P(B|A)P(A))/(P(B)
    Technical Workflow
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    WORDLIST
    DATA
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    RAW
    DATA
    Semantic
    Tagging
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    TOKENS,
    MORPHEMES
    Tokenization
    Cognate
    Detection
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    COGNATE
    SETS
    Alignment
    Analysis
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    SOUND
    CORRESPON-
    DENCES
    HAND [hænd]
    FOOT [fʊt]
    EARTH [ɜːrθ]
    TREE [triː]
    BARK [bɑːrk]
    PROTO-
    FORMS
    Linguistic
    Reconstruction
    7 / 30

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  20. Cognate Detection Automatic Approaches
    Narrowing down the Task
    P(A|B)=(P(B|A)P(A))/(P(B)
    Technical Workflow
    INPUT:
    Multilingual wordlist
    → semantically tagged
    → phonetically transcribed
    → tokenized into phonemes
    OUTPUT:
    Multilingual wordlist
    → identified cognate entries
    assigned to clusters
    → identified cognate entries
    multiply aligned
    7 / 30

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  21. Cognate Detection Automatic Approaches
    Algorithms
    P(A|B)=(P(B|A)P(A))/(P(B)
    8 / 30

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  22. Cognate Detection Automatic Approaches
    Algorithms
    P(A|B)=(P(B|A)P(A))/(P(B)
    Basic Procedure for Multilingual Cognate Detection
    WORDLIST
    DATA
    8 / 30

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  23. Cognate Detection Automatic Approaches
    Algorithms
    P(A|B)=(P(B|A)P(A))/(P(B)
    Basic Procedure for Multilingual Cognate Detection
    WORDLIST
    DATA
    PAIRWISE
    DISTANCES
    BETWEEN
    WORDS
    PAIRWISE
    COMPARISON
    8 / 30

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  24. Cognate Detection Automatic Approaches
    Algorithms
    P(A|B)=(P(B|A)P(A))/(P(B)
    Basic Procedure for Multilingual Cognate Detection
    WORDLIST
    DATA
    PAIRWISE
    DISTANCES
    BETWEEN
    WORDS
    COGNATE
    SETS
    COGNATE
    CLUSTERING
    PAIRWISE
    COMPARISON
    8 / 30

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  25. Cognate Detection Automatic Approaches
    Algorithms
    P(A|B)=(P(B|A)P(A))/(P(B)
    Cognate Clustering
    Analysis
    ID Taxa Word Gloss GlossID IPA
    ... ... ... ... ... ...
    21 German Frau woman 20 frau
    22 Dutch vrouw woman 20 vrɑu
    23 English woman woman 20 wʊmən
    24 Danish kvinde woman 20 kvenə
    25 Swedish kvinna woman 20 kviːna
    26 Norwegian kvine woman 20 kʋinə
    ... ... ... ... ... ...
    8 / 30

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  26. Cognate Detection Automatic Approaches
    Algorithms
    P(A|B)=(P(B|A)P(A))/(P(B)
    Cognate Clustering
    Swedish English Danish Norwegian Dutch German
    kvinna woman kvinde kvine vrouw Frau
    Swedish
    kvina
    0.00 0.69 0.07 0.12 0.71 0.78
    English
    wumin
    0.69 0.00 0.66 0.57 0.68 0.87
    Danish
    kveni
    0.07 0.66 0.00 0.08 0.67 0.71
    Norwegian
    kwini
    0.12 0.57 0.08 0.00 0.75 0.74
    Dutch
    frou
    0.71 0.68 0.67 0.75 0.00 0.17
    German
    frau
    0.78 0.87 0.71 0.74 0.17 0.00
    Analysis
    ID Taxa Word Gloss GlossID IPA
    ... ... ... ... ... ...
    21 German Frau woman 20 frau
    22 Dutch vrouw woman 20 vrɑu
    23 English woman woman 20 wʊmən
    24 Danish kvinde woman 20 kvenə
    25 Swedish kvinna woman 20 kviːna
    26 Norwegian kvine woman 20 kʋinə
    ... ... ... ... ... ...
    8 / 30

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  27. Cognate Detection Automatic Approaches
    Algorithms
    P(A|B)=(P(B|A)P(A))/(P(B)
    Cognate Clustering
    Swedish English Danish Norwegian Dutch German
    kvinna woman kvinde kvine vrouw Frau
    Swedish
    kvina
    0.00 0.69 0.07 0.12 0.71 0.78
    English
    wumin
    0.69 0.00 0.66 0.57 0.68 0.87
    Danish
    kveni
    0.07 0.66 0.00 0.08 0.67 0.71
    Norwegian
    kwini
    0.12 0.57 0.08 0.00 0.75 0.74
    Dutch
    frou
    0.71 0.68 0.67 0.75 0.00 0.17
    German
    frau
    0.78 0.87 0.71 0.74 0.17 0.00
    German Frau frau
    Dutch vrouw vrou
    English woman wumin
    Danish kvinde kveni
    Swedish kvinna kvina
    Norwegian kvine kwini
    8 / 30

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  28. Cognate Detection Automatic Approaches
    Algorithms
    P(A|B)=(P(B|A)P(A))/(P(B)
    Cognate Clustering
    Swedish English Danish Norwegian Dutch German
    kvinna woman kvinde kvine vrouw Frau
    Swedish
    kvina
    0.00 0.69 0.07 0.12 0.71 0.78
    English
    wumin
    0.69 0.00 0.66 0.57 0.68 0.87
    Danish
    kveni
    0.07 0.66 0.00 0.08 0.67 0.71
    Norwegian
    kwini
    0.12 0.57 0.08 0.00 0.75 0.74
    Dutch
    frou
    0.71 0.68 0.67 0.75 0.00 0.17
    German
    frau
    0.78 0.87 0.71 0.74 0.17 0.00
    German Frau frau
    Dutch vrouw vrou
    English woman wumin
    Danish kvinde kveni
    Swedish kvinna kvina
    Norwegian kvine kwini
    8 / 30

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  29. Cognate Detection Automatic Approaches
    Algorithms
    P(A|B)=(P(B|A)P(A))/(P(B)
    Cognate Clustering
    German Frau frau
    Dutch vrouw vrou
    English woman wumin
    Danish kvinde kveni
    Swedish kvinna kvina
    Norwegian kvine kwini
    Analysis
    ID Taxa Word Gloss GlossID IPA CogID
    ... ... ... ... ... ... ...
    21 German Frau woman 20 frau 1
    22 Dutch vrouw woman 20 vrɑu 1
    23 English woman woman 20 wʊmən 2
    24 Danish kvinde woman 20 kvenə 3
    25 Swedish kvinna woman 20 kviːna 3
    26 Norwegian kvine woman 20 kʋinə 3
    ... ... ... ... ... ... ...
    8 / 30

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  30. Cognate Detection Automatic Approaches
    Algorithms
    P(A|B)=(P(B|A)P(A))/(P(B)
    INPUT
    TOKENIZATION
    PREPROCESSING
    LOG-ODDS
    D ISTANCE
    COGNATE
    OUTPUT
    CORRESPONDENCE
    DETECTION USING
    PHONETIC
    ALIGNMENT
    LOOP
    DISTRIBUTION
    LexStat Algorithm (List 2014)
    EXPECTED
    ATTESTED
    DISTRIBUTION
    CALCULATION
    CLUSTERING
    8 / 30

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  31. Cognate Detection Problems
    Problems
    !
    9 / 30

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  32. Cognate Detection Problems
    Applicability
    !
    10 / 30

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  33. Cognate Detection Problems
    Applicability
    !
    Method
    Multilingual?
    No additional
    requirements?
    Freely
    Available?
    Mackay & Kondrak 2005 ✗ ✓ ✗
    Bergsma & Kondrak 2007 ✓ ✓ ✗
    Turchin et al. 2010 ✓ ✓ ✓
    Berg-Kirkpatrick & Klein 2011 ✗ ✓ ✗
    Hauer & Kondrak 2011 ✓ ✓ ✗
    Steiner et al. 2011 ✓ ✓ ✗
    List 2012 & 2014 ✓ ✓ ✓
    Beinborn et al. 2013 ✗ ? ✗
    Bouchard-Côté et al. 2013 ✓ ✗ ✗
    Rama 2013 ✗ ✓ ✗
    Ciobanu & Dinu 2014 ✗ ✓ ✗
    … … … …
    10 / 30

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  34. Cognate Detection Problems
    Applicability
    !
    Method
    Multilingual?
    No additional
    requirements?
    Freely
    Available?
    Mackay & Kondrak 2005 ✗ ✓ ✗
    Bergsma & Kondrak 2007 ✓ ✓ ✗
    Turchin et al. 2010 ✓ ✓ ✓
    Berg-Kirkpatrick & Klein 2011 ✗ ✓ ✗
    Hauer & Kondrak 2011 ✓ ✓ ✗
    Steiner et al. 2011 ✓ ✓ ✗
    List 2012 & 2014 ✓ ✓ ✓
    Beinborn et al. 2013 ✗ ? ✗
    Bouchard-Côté et al. 2013 ✓ ✗ ✗
    Rama 2013 ✗ ✓ ✗
    Ciobanu & Dinu 2014 ✗ ✓ ✗
    … … … …
    10 / 30

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  35. Cognate Detection Problems
    Applicability
    !
    Method
    Multilingual?
    No additional
    requirements?
    Freely
    Available?
    Mackay & Kondrak 2005 ✗ ✓ ✗
    Bergsma & Kondrak 2007 ✓ ✓ ✗
    Turchin et al. 2010 ✓ ✓ ✓
    Berg-Kirkpatrick & Klein 2011 ✗ ✓ ✗
    Hauer & Kondrak 2011 ✓ ✓ ✗
    Steiner et al. 2011 ✓ ✓ ✗
    List 2012 & 2014 ✓ ✓ ✓
    Beinborn et al. 2013 ✗ ? ✗
    Bouchard-Côté et al. 2013 ✓ ✗ ✗
    Rama 2013 ✗ ✓ ✗
    Ciobanu & Dinu 2014 ✗ ✓ ✗
    … … … …
    10 / 30

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  36. Cognate Detection Problems
    Applicability
    !
    Method
    Multilingual?
    No additional
    requirements?
    Freely
    Available?
    Mackay & Kondrak 2005 ✗ ✓ ✗
    Bergsma & Kondrak 2007 ✓ ✓ ✗
    Turchin et al. 2010 ✓ ✓ ✓
    Berg-Kirkpatrick & Klein 2011 ✗ ✓ ✗
    Hauer & Kondrak 2011 ✓ ✓ ✗
    Steiner et al. 2011 ✓ ✓ ✗
    List 2012 & 2014 ✓ ✓ ✓
    Beinborn et al. 2013 ✗ ? ✗
    Bouchard-Côté et al. 2013 ✓ ✗ ✗
    Rama 2013 ✗ ✓ ✗
    Ciobanu & Dinu 2014 ✗ ✓ ✗
    … … … …
    10 / 30

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  37. Cognate Detection Problems
    Transparency
    !
    11 / 30

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  38. Cognate Detection Problems
    Transparency
    !
    Results are often only reported as evaluation scores.
    11 / 30

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  39. Cognate Detection Problems
    Transparency
    !
    Results are often only reported as evaluation scores.
    Examples for individual cognate judgments are rare.
    11 / 30

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  40. Cognate Detection Problems
    Transparency
    !
    Results are often only reported as evaluation scores.
    Examples for individual cognate judgments are rare.
    Supplementary data
    – is often lacking, or
    11 / 30

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  41. Cognate Detection Problems
    Transparency
    !
    Results are often only reported as evaluation scores.
    Examples for individual cognate judgments are rare.
    Supplementary data
    – is often lacking, or
    – not given in a human-readable form.
    11 / 30

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  42. Cognate Detection Problems
    Transparency
    !
    Results are often only reported as evaluation scores.
    Examples for individual cognate judgments are rare.
    Supplementary data
    – is often lacking, or
    – not given in a human-readable form.
    → The results show a great lack of transparency.
    11 / 30

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  43. Cognate Detection Problems
    Comparability
    !
    12 / 30

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  44. Cognate Detection Problems
    Comparability
    !
    Test sets (benchmarks) vary greatly.
    12 / 30

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  45. Cognate Detection Problems
    Comparability
    !
    Test sets (benchmarks) vary greatly.
    Often, only subsets of Dyen et al. (1992) are used.
    12 / 30

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  46. Cognate Detection Problems
    Comparability
    !
    Test sets (benchmarks) vary greatly.
    Often, only subsets of Dyen et al. (1992) are used.
    → It is difficult to compare the performance of the
    methods.
    12 / 30

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  47. Cognate Detection Problems
    Accuracy
    !
    13 / 30

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  48. Cognate Detection Problems
    Accuracy
    !
    Evaluation criteria are not very intuitive and vary greatly.
    13 / 30

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  49. Cognate Detection Problems
    Accuracy
    !
    Evaluation criteria are not very intuitive and vary greatly.
    It is difficult to communicate the results to traditional linguists.
    13 / 30

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  50. Cognate Detection Problems
    Accuracy
    !
    Evaluation criteria are not very intuitive and vary greatly.
    It is difficult to communicate the results to traditional linguists.
    → Many linguists regard automatic cognate detection as
    – “impossible per se”, or
    13 / 30

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  51. Cognate Detection Problems
    Accuracy
    !
    Evaluation criteria are not very intuitive and vary greatly.
    It is difficult to communicate the results to traditional linguists.
    → Many linguists regard automatic cognate detection as
    – “impossible per se”, or
    – as useful as “rolling a dice”.
    13 / 30

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  52. Chances
    14 / 30

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  53. Chances
    14 / 30

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  54. Chances
    14 / 30

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  55. Chances Applicability
    Applicability
    PyPi
    GitHub
    SourceForge
    GoogleCode
    CPAN
    CTAN
    JSAN
    PEAR
    LaunchPad
    15 / 30

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  56. Chances Applicability
    Applicability
    PyPi
    GitHub
    SourceForge
    GoogleCode
    CPAN
    CTAN
    JSAN
    PEAR
    LaunchPad
    It was never easier
    to publish and
    maintain code...
    15 / 30

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  57. Chances Applicability
    LingPy
    PyPi
    GitHub
    SourceForge
    GoogleCode
    CPAN
    CTAN
    JSAN
    PEAR
    LaunchPad
    16 / 30

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  58. Chances Applicability
    LingPy
    PyPi
    GitHub
    SourceForge
    GoogleCode
    CPAN
    CTAN
    JSAN
    PEAR
    LaunchPad
    What is LingPy?
    Python library for automatic tasks in historical linguistics
    project homepage: http://lingpy.org
    code base: https://github.com/lingpy/lingpy
    supports Python2 and Python3
    works on Mac, Linux, and (basically also) Windows
    current release: 2.3
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  59. Chances Applicability
    LingPy
    PyPi
    GitHub
    SourceForge
    GoogleCode
    CPAN
    CTAN
    JSAN
    PEAR
    LaunchPad
    What does LingPy offer?
    tokenization of phonetic sequences
    phonetic alignment analyses (List 2012a)
    automatic cognate detection (Turchin 2010, List 2012b)
    automatic borrowing detection (List et al. 2014)
    basic routines for the evaluation of automatic methods
    plotting routines for interactive visualizations
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  60. Chances Transparency
    Transparency
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  61. Chances Transparency
    Interactive Presentation of Results
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  62. Chances Transparency
    Interactive Presentation of Results
    Alignments offer a unique perspective on results of
    cognate detection analyses.
    JavaScript and HTML5 offer unique ways for
    interactive data visualization.
    At the moment, we develop JavaScript tools that
    – visualize phonetic alignments of cognate sets, and
    – even allow to edit the data online.
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  63. Chances Comparability
    Comparability
    ML
    BAYES
    ? !
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  64. Chances Comparability
    Benchmark Databases for Historical Linguistics
    ML
    BAYES
    ? !
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  65. Chances Comparability
    Benchmark Databases for Historical Linguistics
    ML
    BAYES
    ? !
    First benchmark databases have been compiled and published:
    Benchmark Database of Phonetic Alignments (BDPA, List & Prokić
    2014, http://alignments.lingpy.org)
    Benchmark Database for Cognate Detection (BDCD, presented in
    List 2014, http://sequencecomparison.github.io).
    Benchmark Database for Linguistic Reconstruction (BDLR, in
    preparation).
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  66. Chances Comparability
    Benchmark Databases for Historical Linguistics
    ML
    BAYES
    ? !
    All data is
    given in phonetic transcriptions (IPA),
    tokenized into phonemic units,
    freely available for download, and
    can be directly used in LingPy.
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  67. Chances Accuracy
    Accuracy
    *h₂
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  68. Chances Accuracy
    Performance of Cognate Detection Algorithms
    *h₂
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  69. Chances Accuracy
    Performance of Cognate Detection Algorithms
    *h₂ B-Cubed F-Scores on BDCD Benchmark (List 2014)
    Bai
    (Tibeto-Burman)
    Indo-European
    Japanese and
    Ryukyu Ob-Ugrian
    Austronesian
    Sinitic
    (Chinese Dialects)
    60
    65
    70
    75
    80
    85
    90
    95
    Turchin
    NED
    SCA
    LexStat
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  70. Chances Accuracy
    Performance of Cognate Detection Algorithms
    *h₂ B-Cubed F-Scores on BDCD Benchmark (List 2014)
    Bai
    (Tibeto-Burman)
    Indo-European
    Japanese and
    Ryukyu Ob-Ugrian
    Austronesian
    Sinitic
    (Chinese Dialects)
    60
    65
    70
    75
    80
    85
    90
    95
    Turchin
    NED
    SCA
    LexStat
    75%
    93%
    92%
    81%
    89%
    81%
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  71. Chances Accuracy
    Performance of Cognate Detection Algorithms
    *h₂ B-Cubed F-Scores on BDCD Benchmark (List 2014)
    Bai
    (Tibeto-Burman)
    Indo-European
    Japanese and
    Ryukyu Ob-Ugrian
    Austronesian
    Sinitic
    (Chinese Dialects)
    60
    65
    70
    75
    80
    85
    90
    95
    Turchin
    NED
    SCA
    LexStat
    75%
    93%
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  72. P(A|B)=(P(B|A)P(A))/(P(B)
    Challenges
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  73. Challenges Within Cognacy
    Within Cognacy
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  74. Challenges Within Cognacy
    Within Cognacy
    We need to enhance our
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  75. Challenges Within Cognacy
    Within Cognacy
    We need to enhance our
    lexical databases (amount and quality of data),
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  76. Challenges Within Cognacy
    Within Cognacy
    We need to enhance our
    lexical databases (amount and quality of data),
    cognate detection algorithms (accessibility and performance), and
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  77. Challenges Within Cognacy
    Within Cognacy
    We need to enhance our
    lexical databases (amount and quality of data),
    cognate detection algorithms (accessibility and performance), and
    ways to present the results (interactive visualizations).
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  78. Challenges Beyond Cognacy
    Beyond Cognacy
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  79. Challenges Beyond Cognacy
    Beyond Cognacy
    German m oː n t -
    English m uː n - -
    Danish m ɔː n - ə
    Swedish m oː n - e
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  80. Challenges Beyond Cognacy
    Beyond Cognacy
    German m oː n t -
    English m uː n - -
    Danish m ɔː n - ə
    Swedish m oː n - e
    Fúzhōu ŋ u o ʔ ⁵ - - - - - - - - - -
    Měixiàn ŋ i a t ⁵ - - - - - k u o ŋ ⁴⁴
    Guǎngzhōu j - y t ² l - œ ŋ ²² - - - - -
    Běijīng - y ɛ - ⁵¹ l i ɑ ŋ - - - - - -
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  81. Challenges Beyond Cognacy
    Beyond Cognacy
    German m oː n t -
    English m uː n - -
    Danish m ɔː n - ə
    Swedish m oː n - e
    Fúzhōu ŋ u o ʔ ⁵ - - - - - - - - - -
    Měixiàn ŋ i a t ⁵ - - - - - k u o ŋ ⁴⁴
    Guǎngzhōu j - y t ² l - œ ŋ ²² - - - - -
    Běijīng - y ɛ - ⁵¹ l i ɑ ŋ - - - - - -
    "MOON"
    "MOON"
    "SHINE" "LIGHT"
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  82. Challenges Beyond Cognacy
    Beyond Cognacy
    Fúzhōu
    Měixiàn
    Guǎngzhōu
    Běijīng
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  83. Challenges Beyond Cognacy
    Beyond Cognacy
    Fúzhōu
    Měixiàn
    Guǎngzhōu
    Běijīng
    INNO
    VATIO
    N
    INNO
    VATIO
    N
    INNO
    VATIO
    N
    BO
    RRO
    W
    ING
    LO
    SS
    INNO
    VATIO
    N
    INNO
    VATIO
    N
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  84. Challenges Beyond Cognacy
    Lexical Change
    SEMANTIC CHANGE
    MORPHOLOGICAL CHANGE
    S
    T
    R
    A
    T
    IC
    C
    H
    A
    N
    G
    E
    Three Dimensions of Lexical Change (Gévaudan 2007)
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  85. Challenges Beyond Cognacy
    Lexical Change
    Stratic
    Morphological
    Semantic
    Relation Biolog. Term continuity
    traditional notion of cognacy - + +/- +/-
    cognacy à la Swadesh - + +/- +
    automatic cognate detection - +/- +/- +
    direct cognate relation orthology + + +
    oblique cognate relation paralogy + - +
    etymological relation homology +/- +/- +/-
    oblique etymological relation xenology - +/- +/-
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  86. Challenges Beyond Cognacy
    Inferring Lexical Change Scenarios
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  87. Challenges Beyond Cognacy
    Inferring Lexical Change Scenarios
    In order to go beyond cognacy, we need methods for
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  88. Challenges Beyond Cognacy
    Inferring Lexical Change Scenarios
    In order to go beyond cognacy, we need methods for
    borrowing detection (stratic aspect),
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  89. Challenges Beyond Cognacy
    Inferring Lexical Change Scenarios
    In order to go beyond cognacy, we need methods for
    borrowing detection (stratic aspect),
    partial cognate inference (morphological aspect), and
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  90. Challenges Beyond Cognacy
    Inferring Lexical Change Scenarios
    In order to go beyond cognacy, we need methods for
    borrowing detection (stratic aspect),
    partial cognate inference (morphological aspect), and
    cross-semantic cognate inference (semantic aspect).
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  91. Challenges Beyond Cognacy
    Inferring Lexical Change Scenarios
    In order to go beyond cognacy, we need methods for
    borrowing detection (stratic aspect),
    partial cognate inference (morphological aspect), and
    cross-semantic cognate inference (semantic aspect).
    Following the lead of evolutionary biology, these methods should be
    combined under a unified framework of tree reconciliation (Page &
    Cotton 2002) in historical linguistics.
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  92. Challenges Beyond Cognacy
    Tree Reconciliation
    Fúzhōu
    Měixiàn
    Guǎngzhōu
    Běijīng Fúzhōu
    Měixiàn
    Guǎngzhōu
    Běijīng
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  93. Challenges Beyond Cognacy
    Tree Reconciliation
    Fúzhōu
    Měixiàn
    Guǎngzhōu
    Běijīng Fúzhōu
    Měixiàn
    Guǎngzhōu
    Běijīng
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  94. Challenges Beyond Cognacy
    Tree Reconciliation
    Fúzhōu
    Měixiàn
    Guǎngzhōu
    Běijīng
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  95. Challenges Beyond Cognacy
    Tree Reconciliation
    Fúzhōu
    Měixiàn
    Guǎngzhōu
    Běijīng
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  96. Challenges Beyond Cognacy
    Tree Reconciliation
    LOSS
    INNO
    VATIO
    N
    INNO
    VATIO
    N
    BORROWING
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  97. Challenges Beyond Cognacy
    Tree Reconciliation
    PHYLOGENETIC
    RECONSTRUC-
    TION
    COGNATE
    (=HOMOLOG)
    DETECTION
    COGNATE
    TREE
    RECONCILIATION
    General Workflow for the Inference of Lexical Change Scenarios
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  98. Conclusion
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  99. Conclusion
    Automatic cognate detection is still in its infancy, yet the child is
    constantly growing.
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  100. Conclusion
    Automatic cognate detection is still in its infancy, yet the child is
    constantly growing.
    Enhancing the applicability, transparency, comparability, and
    accuracy of cognate detection methods is a goal that can be
    achieved in the near future.
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  101. Conclusion
    Automatic cognate detection is still in its infancy, yet the child is
    constantly growing.
    Enhancing the applicability, transparency, comparability, and
    accuracy of cognate detection methods is a goal that can be
    achieved in the near future.
    The greatest challenge arises from the complexity of lexical
    change processes.
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  102. Conclusion
    Automatic cognate detection is still in its infancy, yet the child is
    constantly growing.
    Enhancing the applicability, transparency, comparability, and
    accuracy of cognate detection methods is a goal that can be
    achieved in the near future.
    The greatest challenge arises from the complexity of lexical
    change processes.
    More realistic approaches that go beyond cognacy should be able
    to handle variation along the stratic, the morphological, and the
    semantic dimension of lexical change.
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  103. Conclusion
    Automatic cognate detection is still in its infancy, yet the child is
    constantly growing.
    Enhancing the applicability, transparency, comparability, and
    accuracy of cognate detection methods is a goal that can be
    achieved in the near future.
    The greatest challenge arises from the complexity of lexical
    change processes.
    More realistic approaches that go beyond cognacy should be able
    to handle variation along the stratic, the morphological, and the
    semantic dimension of lexical change.
    Evolutionary biology offers frameworks that could be employed to
    achieve these goals, yet it is not entirely clear whether and how
    this is possible.
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  104. Thank You for Listening!
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