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A sound-change-based phylogeny of the Tukanoan language family

A sound-change-based phylogeny of the Tukanoan language family

Talk by Thiago Chacon and Johann-Mattis List, presented at the workshop "Towards a Global Language Phylogeny", 21-23 October, Max Planck Institute for the Science of Human History, Jena.

Johann-Mattis List

October 21, 2015
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  1. A Sound-Change-Based Phylogeny of
    the Tukanoan Language Family
    Using Ordered Multistate Models for Phylogenetic Reconstruction
    Thiago Chacon and Johann-Mattis List

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  2. Introduction

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  3. Goals of the Study
    The goals of this study is to present a phylogenetic model that can infer trees from sound
    changes
    Other models used for inferring trees from phonological data are
    ● Hruschka et al. (2015)
    ● Wheeler and Whiteley (2015)
    Our model differs from these approaches in the following important ways:
    ● We do neither use raw sequences (Wheeler and Whiteley 2015) nor aligned
    sequences (Hruschka et al. 2015). Instead we use data on sound change patterns.
    ● In this, we follow Barbaçon et. al. (2013:165): “linguistic phylogeny estimation — and
    studies of phylogeny estimation methods in linguistics — need to be informed by
    linguistic scholarship”

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  4. The Tukano Language Family
    29 languages
    15 with reasonable documentation
    Northwest Amazon

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  6. The Tukano Language Family
    Disputed Internal classification
    - Methods
    - Heuristics (Mason 1950)
    - Lexicostatistics (Waltz and Wheeler 1972, Ramirez 1997)
    - Sound Innovations (Malone 1987, Chacon 2014)
    - Major branches:
    - 2: ET and WT (Mason 1950, Chacon 2014)
    - 3: ET, WT and CT (Waltz and Wheeler 1972, Malone 1987, Barnes 1999)
    - Internal classification of major branches:
    - 3 minor branches in each branch (Chacon 2014)

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  7. Classification
    by Chacon
    (2014), based on
    shared
    innnovations in
    sound-change
    processes

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  8. Genetic grouping and subgrouping by the traditional
    comparative method
    1. Word-lists
    2. Cognate sets
    3. Sound correspondence sets…
    4. Evidence for genetic relationship: systematic form-meaning relations
    5. Reconstruction of proto-forms
    6. Analysis of complementary distribution
    7. Refining reconstruction
    8. Proto-form to reflexes
    9. Proposal of intermediate proto-forms...
    10. Subgrouping: identification of shared sound changes followed by
    interpretation of “remarkable” changes favouring certain subgroups.

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  9. Genetic grouping and subgrouping with the
    traditional comparative method
    Cognate set
    Kor Sek Sio Mai Tan Bas Des Kub Tuk Kar Wan P-T Gloss
    pia p’ia p’ia ʔbia bia bia bia bia bia bia bia *p’ia CHILI
    jeha jeha jiha jiha - jiba jeba jeba je’pa jepa ja’pa *jip’a LAND/
    GROUND

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  10. Genetic grouping and subgrouping with the
    traditional comparative method
    Sound correspondence sets and proto-forms
    Kor Sek Sio Mai Tan Bas Des Kub Tuk Kar Wan Context P-T
    p p’ p’ ʔb b b b b b b b #_ *p’
    h h h h h b b b ʔp p ʔp V_ *p’

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  11. Genetic grouping and subgrouping with the
    traditional comparative method
    A unique change identifies the ET subgroup:
    *p’ > b / #_
    Another unique change identifies an ET subgroup
    *p’ > b / V_

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  12. Changes for PT *p /#_

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  13. Changes for PT *p /V_V

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  14. Genetic grouping and subgrouping with the
    traditional comparative method
    ● Homoplasy: Not all sound changes occur only once, they can occur multiple
    times, and some are so frequent that they do not provide any evidence for
    subgrouping (think of kentum-satem languages). But there’s often
    disagreement among scholars as to what are innovations and what not.
    ● What counts as “remarkable”: Scholars have not come up with a rigorous
    procedure that would justify why some innovations should be more important
    for subgrouping than others.
    ● Circularity: Since reconstruction usually involves a certain amount of
    subgrouping (at least in the researcher’s heads), one runs the risk of making
    circular arguments for a certain subgroup due to a certain reconstruction.
    ● Risk of cherry picking: In the end, all scholars run the danger of selecting
    innovations just according to their original hypotheses.

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  15. The model we propose here is a first step towards making the subgrouping
    enterprise more objective:
    - “Remarkable” is replaced by a definite principle of overall uniqueness:
    subgroups are established by the greatest number of more unique changes
    shared by a set of languages
    - Homoplasy is captured as the most frequent changes recurring over a set of
    languages that also share more unique changes
    - No personal bias towards cherry picking
    Circularity between reconstruction and subgrouping is to some degree still
    present, but...
    - “Phonetic Drifts” give a phonetic measure of the likelihood of a change
    - The algorithm evaluates other changes that were not at the focus of the
    analyst when establishing subgroupings

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  16. Materials

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  17. Language Data
    Data preparation
    i. phonemic representation
    ii. identifying cognate sets: 150 cognates
    iii. extracting sound correspondences: 33 sets
    iv. reconstruction of proto-sounds: 18 consonants and 42 reflexes
    v. Preparation of “phonetic drifts” for creating networks of sound transitions

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  18. Phonetic drifts
    “Phonetic drifts” represent sound changes as internally organized in different
    stages within a pool of potential articulatory variations.
    From the internal organization of drifts, it is possible to infer speciation events from
    a proto-sound to a set of reflexes:
    *k > k, t∫, s, x *k
    k t∫ s x
    L1 L2 L3 L4

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  19. Phonetic drifts
    This is only possible due to the very nature of sound changes, which are
    Regular: or at least overwhelmingly
    Gradual: following more or less discrete steps from T1 to T2… Tn
    Phonetically blind: overwhelmingly following from universal principles of
    phonetics
    Directional: A > B but B > A

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  20. Phonetic drifts
    The principles underlying the drifts are the following
    (1) Teleology: from proto-sound to reflexes
    (2) Intermediate stages: *A > C → **A > *B > C
    (3) Directionality
    (4) Context dependency
    (5) Competing pathways of change: *A > B > D or *A > C > D

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  21. Phonetic drift
    PT: *k
    Reflexes: t∫, s, h
    k > t∫ > ∫ > s > h
    k > t∫ > ∫ > h
    k > kx > x > h

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  22. Methods

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  23. Weighted Directed Transitions for Character States
    Our model assumes multiple character states with state
    transitions which are
    ● directed, and
    ● weighted.
    We further allow for unattested character states which
    we include explicitly into the model.

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  24. Weighted Directed Transitions for Character States

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  25. Weighted Directed Transitions for Character States

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  26. Weighted Directed Transitions for Character States

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  27. Weighted Directed Transitions for Character States

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  28. From Proto-Forms and Reflexes to Characters
    ● Phonetic drifts were converted into a sound network, treating the
    proto-form as just another sound.
    ● This pattern is then converted into a transition matrix, by
    ● converting it into a graph first
    ● and then calculating character transition weights by computing the
    shortest path length between the characters
    ● if no shortest path can be found, the transition is given a high
    penalty

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  29. From Proto-Forms and Reflexes to Characters

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  30. Tree Search Heuristics
    Since it is not feasible to search through the whole tree space when dealing with
    more than 10 languages, we need to search heuristically.
    The strategy we use follows the following schema:
    ● start from a random tree, and
    ○ create more trees by swapping nodes in the tree
    ○ retain the best trees (in terms of parsimony scores) and create more trees from them
    ○ create more random trees to avoid to get stuck in a local maximum
    ● stop the tree search and return the best trees, if the researcher has had
    enough

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  31. This plot illustrates how the
    model searches the tree
    space for the first 6000
    trees visited. Note the
    nearly constant amount of
    badly scoring trees,
    reflecting the constant
    amount of random trees
    which are generated to
    make sure that the model
    does not get stuck in a
    local optimum.
    Tree Search Heuristics

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  32. Implementation, Analysis, and Evaluation
    ● Code is implemented in Python, as a LingPy plugin (later to be included
    regularly in LingPy).
    ● We analyzed three different models (searching 500 000 trees for each), in
    order to check for the effects of directionality and weights in networks:
    ○ FITCH: a simple parsimony that penalizes every transition with 1
    ○ SANKOFF: a weighted parsimony model that penalizes transitions by calculating the shortest
    path in the sound transition network, but with the sound transition network being treated as an
    undirected network
    ○ WDT (weighted directed transitions): The model which we described before.
    ● We evaluate the performance of each model by comparing the reconstruction
    quality (ancestral state reconstruction for the best trees), and the trees
    themselves (qualitative evaluation).

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  33. Results

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  34. General Results: Numbers
    Model Parsimony Score Most
    Parsimonious
    Trees
    Homoplasy Reconstruction
    Success
    FITCH 107 716 0.67 35%
    SANKOFF 148 1019 0.82 33%
    WDT 182 18 1.9 90%

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  35. General Results: Networks of Sound Transitions
    With the result of a given analysis (a tree or a set of trees), we can
    calculate for each sound transition how frequently it occurs in the
    data for the given tree. This is interesting both with respect to
    questions regarding sound change frequencies, but also with
    respect to the quality of our analysis, since we would assume that
    those changes which occur most frequently are also those changes
    which are generally frequent and lead to high degrees of
    homoplasy in parsimony analyses.

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  36. Full network of attested
    sound transitions for the
    WDT analysis.
    General Results: Networks of Sound Transitions

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  37. General Results: Networks of Sound Transitions
    Sub-network of attested
    sound transitions for the
    WDT analysis (dental
    cluster).

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  38. General Results: Networks of Sound Transitions
    Sub-network of attested
    sound transitions for the
    WDT analysis (labial
    cluster).

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  39. Specific Results: Networks of Sound Transitions
    Sub-network of attested
    sound transitions for the
    WDT analysis (velar
    cluster).

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  40. General Results: Networks of Sound Transitions
    Sub-network of attested
    sound transitions for the
    WDT analysis (affricate
    cluster).

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  41. General Results: Visualizing the Findings
    ● An interactive web-application was created to allow for a quick
    inspection of the results.
    ● It shows individual evolutionary scenarios inferred for each of the
    models and the corresponding consensus tree.
    ● It shows also a summary of each model with all changes inferred
    for each node and a detailed listing of those sounds that have
    changed by then according to the given model.
    ● The application can be launched via: http://digling.org/tukano/

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  42. Specific Results
    Overall performance of tree topology
    WDT > FITCH > Sankoff
    excellent unacceptable

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  43. SANKOFF: Majority Rule Consensus
    General Results: Sankoff Trees

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  44. Specific Results
    SANKOFF
    - Overall failure in identifying major and intermediate subgroups
    - Only surface similarities seem to have influenced the tree
    - A little better performance regarding more shallow subgroups
    - Perfect match with manual classification regarding Western-ET subgroup

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  45. FITCH: Majority Rule Consensus
    Specific Results: FITCH Trees

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  46. Specific Results
    FITCH
    - A little better, but still unacceptable classification
    - ET and WT was not fully captured
    - ET languages BAS and MAK were wrongly classified as an outgroup
    - Good tree for WT and E-ET languages

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  47. WDT: Majority Rule Consensus
    General Results: WDT Trees

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  48. Specific Results
    WDT
    - Excellent tree!
    - WT vs. ET split was neatly captured!
    - WT internal classification matches perfectly with manual classification
    - 3 ET subgroups!
    - Kub and Tan as an ET outgroup, confirming alternative expectations!
    - Very consistent subgrouping in Western-ET and Eastern-ET

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  49. WDT vs. Chacon 2014: analysis of sound changes
    Chacon 2014 WDT
    *j > t∫ unique unique
    *t’ > d > r
    *h > Ø
    parallel parallel
    *t > d unique parallel
    *C’ > v’C retention unique + parallel
    *p’ > p vs. b overlooked unique

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  50. Examples of analysis of phonological patterns
    Relative Chronology and Chain Shifts
    (1a) *h > Ø
    (1b) *s > h
    Mergers and horizontal diffusion
    *t∫, *ts, *s

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  51. Outlook

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  52. Further experimentations:
    ○ general networks instead of individual networks for each character (“local” vs. “global”
    networks)
    ○ constraining vs. expanding intermediate stages in phonetic transitions
    ○ weighting sound transitions, e.g.:
    ■ assimilation +1 incrementation vs. dissimilation +5 incrementation
    ■ articulatorily biased change +1 vs. acoustically biased change +2
    ○ other linguistic families:
    ■ widely known, e.g. Romance vs. poorly known, e.g. Arawak
    ■ shallow vs. deeper time depth
    ■ few (around 10) vs. many languages (40+)
    Research and database on the typology of sound changes
    Directionality seems to be the key. Do we get directionality into probabilistic
    models?

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  53. Thank You for Listening!

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