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CLICS 2.0: A computer-assisted framework for the investigation of lexical motivation patterns investigation of lexical motivation patterns

CLICS 2.0: A computer-assisted framework for the investigation of lexical motivation patterns investigation of lexical motivation patterns

Talk, held at the workshop "Semantic maps: where do we stand, and where are we going?" (Université de Liège, 2018/06/26-28).

Johann-Mattis List

June 27, 2018
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  1. CLICS²
    A computer-assisted framework for the investigation of lexical
    motivation patterns
    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
    2018-06-27
    very
    long
    title
    P(A|B)=P(B|A)...
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  2. From Semantic Maps to Cross-Linguistic Polysemy Networks
    From Semantic Maps...
    ... to Cross-Linguistic Polysemies
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  3. From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
    Early Accounts: People and Ideas
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  4. From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
    Early Accounts: People and Ideas
    Haspelmath (2003): The geometry of grammatical meaning.
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  5. From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
    Early Accounts: People and Ideas
    Haspelmath (2003): The geometry of grammatical meaning.
    François (2008): Semantic maps and the typology of colexification.
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  6. From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
    Early Accounts: People and Ideas
    Haspelmath (2003): The geometry of grammatical meaning.
    François (2008): Semantic maps and the typology of colexification.
    Cysouw (2010): Drawing networks from recurrent polysemies.
    3 / 34

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  7. From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
    Early Accounts: People and Ideas
    Haspelmath (2003): The geometry of grammatical meaning.
    François (2008): Semantic maps and the typology of colexification.
    Cysouw (2010): Drawing networks from recurrent polysemies.
    Steiner, Stadler, and Cysouw (2011): A pipeline for computational
    historical linguistics.
    3 / 34

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  8. From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
    Early Accounts: People and Ideas
    Haspelmath (2003): The geometry of grammatical meaning.
    François (2008): Semantic maps and the typology of colexification.
    Cysouw (2010): Drawing networks from recurrent polysemies.
    Steiner, Stadler, and Cysouw (2011): A pipeline for computational
    historical linguistics.
    Urban (2011): Assymetries in overt marking and directionality in
    semantic change.
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  9. From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
    Early Accounts: Data
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  10. From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
    Early Accounts: Data
    Intercontinental Dictionary Series (IDS, Key and Comrie 2016)
    offers 1310 concepts translated into about 360 languages, an earlier
    version offered ca. 200 languages.
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  11. From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
    Early Accounts: Data
    Intercontinental Dictionary Series (IDS, Key and Comrie 2016)
    offers 1310 concepts translated into about 360 languages, an earlier
    version offered ca. 200 languages.
    World Loanword Typology (WOLD, Haspelmath and Tadmor 2009)
    offers 1430 concepts translated into 41 languages (some overlap
    with IDS).
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  12. From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
    Early Accounts: Techniques
    Steiner, Stadler, and Cysouw (2011) present the idea to model
    similarities between concepts by constructing a matrix from parts of
    the IDS data that shows how often individual languages colexify
    certain concepts.
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  13. From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
    Early Accounts: Techniques
    Steiner, Stadler, and Cysouw (2011) present the idea to model
    similarities between concepts by constructing a matrix from parts of
    the IDS data that shows how often individual languages colexify
    certain concepts.
    Cysouw (2010) shows how to use polysemy data to draw networks.
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  14. From Semantic Maps to Cross-Linguistic Polysemy Networks Initial Ideas
    Initial Ideas
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  15. From Semantic Maps to Cross-Linguistic Polysemy Networks Initial Ideas
    Initial Ideas
    List, Terhalle, and Urban (2013) build on ideas of Cysouw (2010)
    and Steiner, Stadler and Cysouw (2011) in using IDS data for
    polysemy studies and in using network techniques to study the data.
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  16. From Semantic Maps to Cross-Linguistic Polysemy Networks Initial Ideas
    Initial Ideas
    List, Terhalle, and Urban (2013) build on ideas of Cysouw (2010)
    and Steiner, Stadler and Cysouw (2011) in using IDS data for
    polysemy studies and in using network techniques to study the data.
    In contrast to earlier approaches, they use techniques for
    community detection (Girvan and Newman 2002) to further analyse
    the network, and to partition the concepts into communities which
    seem to make intuitively sense, reminding of naturally derived
    semantic fields.
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  17. From Semantic Maps to Cross-Linguistic Polysemy Networks Further Ideas
    Further Ideas
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  18. From Semantic Maps to Cross-Linguistic Polysemy Networks Further Ideas
    Further Ideas
    Mayer, List, Terhalle, and Urban (2014) present an interactive way
    to visualize cross-linguistic colexification data.
    7 / 34

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  19. From Semantic Maps to Cross-Linguistic Polysemy Networks Further Ideas
    Further Ideas
    Mayer, List, Terhalle, and Urban (2014) present an interactive way
    to visualize cross-linguistic colexification data.
    List, Mayer, Terhalle, and Urban (2014) publish the database and
    the web-application online, under the name CLICS (Database of
    Cross-Linguistic Colexifications).
    7 / 34

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  20. From Semantic Maps to Cross-Linguistic Polysemy Networks Further Ideas
    Further Ideas
    Mayer, List, Terhalle, and Urban (2014) present an interactive way
    to visualize cross-linguistic colexification data.
    List, Mayer, Terhalle, and Urban (2014) publish the database and
    the web-application online, under the name CLICS (Database of
    Cross-Linguistic Colexifications).
    In contrast to earlier attempts, they increase the data by merging
    IDS, WOLD, and additional datasets, thus containing 220
    languages in total.
    7 / 34

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  21. From Semantic Maps to Cross-Linguistic Polysemy Networks Further Ideas
    Further Ideas
    Mayer, List, Terhalle, and Urban (2014) present an interactive way
    to visualize cross-linguistic colexification data.
    List, Mayer, Terhalle, and Urban (2014) publish the database and
    the web-application online, under the name CLICS (Database of
    Cross-Linguistic Colexifications).
    In contrast to earlier attempts, they increase the data by merging
    IDS, WOLD, and additional datasets, thus containing 220
    languages in total.
    They also improve the community detection procedure by using
    Infomap (Rosvall and Bergstrom 2008), an advanced algorithm
    based on random walks in complex networks.
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  22. CLICS 1.0
    CLICS 1.0
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  23. CLICS 1.0 Data
    Data
    9 / 34

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  24. CLICS 1.0 Data
    Data
    IDS (Key and Comrie 2007 version), of 233 language varieties, 178
    included in CLICS.
    9 / 34

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  25. CLICS 1.0 Data
    Data
    IDS (Key and Comrie 2007 version), of 233 language varieties, 178
    included in CLICS.
    WOLD (Haspelmath and Tadmor 2009), of 41 languages in WOLD,
    33 are included in CLICS.
    9 / 34

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  26. CLICS 1.0 Data
    Data
    IDS (Key and Comrie 2007 version), of 233 language varieties, 178
    included in CLICS.
    WOLD (Haspelmath and Tadmor 2009), of 41 languages in WOLD,
    33 are included in CLICS.
    Logos Dictionary (Logos Group), of dictionaries for more than 60
    different languages, 4 languages were manually extracted and
    included in CLICS.
    9 / 34

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  27. CLICS 1.0 Data
    Data
    IDS (Key and Comrie 2007 version), of 233 language varieties, 178
    included in CLICS.
    WOLD (Haspelmath and Tadmor 2009), of 41 languages in WOLD,
    33 are included in CLICS.
    Logos Dictionary (Logos Group), of dictionaries for more than 60
    different languages, 4 languages were manually extracted and
    included in CLICS.
    Språkbanken project (University of Gothenburg) offers 8 word lists
    for SEA languages, 6 were included in CLICS.
    9 / 34

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  28. CLICS 1.0 Methods
    Methods
    Problems
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  29. CLICS 1.0 Methods
    Methods
    Problems
    (A) Data cannot be displayed fully, complexity needs to be reduced.
    (B) Data is noisy and needs to be corrected.
    10 / 34

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  30. CLICS 1.0 Methods
    Methods
    Problems
    (A) Data cannot be displayed fully, complexity needs to be reduced.
    (B) Data is noisy and needs to be corrected.
    Solutions
    10 / 34

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  31. CLICS 1.0 Methods
    Methods
    Problems
    (A) Data cannot be displayed fully, complexity needs to be reduced.
    (B) Data is noisy and needs to be corrected.
    Solutions
    (A) Show communities instead of showing all the data, offer a
    subgraph-view that cuts out the nearest neighbors of one concept to
    compensate for data loss in the community view.
    (B) Filter by language families and weight the concept links by
    frequency of occurrence, following Dellert’s (2014) suggestion. This
    will cut most of the links resulting from homophony and leaves the
    links which are due to polysemy.
    10 / 34

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  32. CLICS 1.0 Interface
    Interface
    11 / 34

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  33. CLICS 1.0 Interface
    Interface
    Interface is written in JavaScript for the visualizations and PhP for
    querying the data.
    11 / 34

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  34. CLICS 1.0 Interface
    Interface
    Interface is written in JavaScript for the visualizations and PhP for
    querying the data.
    The interactive component of the network browser was specifically
    designed for CLICS and builds on the D3 framework by Bostock et
    al. (2011).
    11 / 34

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  35. CLICS 1.0 Interface
    Interface
    Interface is written in JavaScript for the visualizations and PhP for
    querying the data.
    The interactive component of the network browser was specifically
    designed for CLICS and builds on the D3 framework by Bostock et
    al. (2011).
    The underlying network with the inferred communities is offered for
    download from the website, and the whole code which was used to
    create the website is available for download at
    http://github.com/clics/clics.
    11 / 34

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  36. CLICS 1.0 Interface
    Interface
    Interface is written in JavaScript for the visualizations and PhP for
    querying the data.
    The interactive component of the network browser was specifically
    designed for CLICS and builds on the D3 framework by Bostock et
    al. (2011).
    The underlying network with the inferred communities is offered for
    download from the website, and the whole code which was used to
    create the website is available for download at
    http://github.com/clics/clics.
    The full wordlists underlying the original CLICS database are now
    also available from Zenodo (published in List 2018,
    https://zenodo.org/record/1194088).
    11 / 34

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  37. CLICS 1.0 Interface
    CLICS DEMO
    12 / 34

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  38. CLICS²
    CLICS²
    13 / 34

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  39. CLICS² Motivation
    Motivation
    14 / 34

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  40. CLICS² Motivation
    Motivation
    Problems in CLICS 1.0
    difficult to curate (error-correction, linking data, adding data)
    14 / 34

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  41. CLICS² Motivation
    Motivation
    Problems in CLICS 1.0
    difficult to curate (error-correction, linking data, adding data)
    difficult to collaborate (the CLICS team is separated and everybody
    is extremely busy with things other than CLICS
    14 / 34

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  42. CLICS² Motivation
    Motivation
    Problems in CLICS 1.0
    difficult to curate (error-correction, linking data, adding data)
    difficult to collaborate (the CLICS team is separated and everybody
    is extremely busy with things other than CLICS
    difficult to communicate (not all users understand how we arrived at
    the data, and often think that it is us who messed up datasets, etc.,
    although we only take the data to produce something new out of it)
    14 / 34

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  43. CLICS² Motivation
    Motivation
    Problems in CLICS 1.0
    difficult to curate (error-correction, linking data, adding data)
    difficult to collaborate (the CLICS team is separated and everybody
    is extremely busy with things other than CLICS
    difficult to communicate (not all users understand how we arrived at
    the data, and often think that it is us who messed up datasets, etc.,
    although we only take the data to produce something new out of it)
    difficult to expand (new datasets cannot be added without having a
    true guiding principle)
    14 / 34

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  44. CLICS² Motivation
    Motivation
    Problems in CLICS 1.0
    difficult to curate (error-correction, linking data, adding data)
    difficult to collaborate (the CLICS team is separated and everybody
    is extremely busy with things other than CLICS
    difficult to communicate (not all users understand how we arrived at
    the data, and often think that it is us who messed up datasets, etc.,
    although we only take the data to produce something new out of it)
    difficult to expand (new datasets cannot be added without having a
    true guiding principle)
    difficult to catch up (we know much, much better now, how to curate
    datasets, but we did not know this when preparing CLICS initially)
    14 / 34

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  45. CLICS² Ideas
    Ideas
    15 / 34

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  46. CLICS² Ideas
    Ideas
    use the state of the art of available wordlist data
    15 / 34

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  47. CLICS² Ideas
    Ideas
    use the state of the art of available wordlist data
    separate data from display (CLICS² does not host data, but simply
    uses it)
    15 / 34

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  48. CLICS² Ideas
    Ideas
    use the state of the art of available wordlist data
    separate data from display (CLICS² does not host data, but simply
    uses it)
    curate data following the recommendations developed for the
    Cross-Linguistic Data Formats (CLDF, http://cldf.clld.org)
    initiative (Forkel et al. 2017)
    15 / 34

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  49. CLICS² Ideas
    Ideas
    use the state of the art of available wordlist data
    separate data from display (CLICS² does not host data, but simply
    uses it)
    curate data following the recommendations developed for the
    Cross-Linguistic Data Formats (CLDF, http://cldf.clld.org)
    initiative (Forkel et al. 2017)
    curate the code and the data with help of a transparent API
    15 / 34

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  50. CLICS² Ideas
    Ideas
    use the state of the art of available wordlist data
    separate data from display (CLICS² does not host data, but simply
    uses it)
    curate data following the recommendations developed for the
    Cross-Linguistic Data Formats (CLDF, http://cldf.clld.org)
    initiative (Forkel et al. 2017)
    curate the code and the data with help of a transparent API
    regularly release the data in release circles of about 1 per year
    (following the practice of Glottolog and other CLLD projects)
    15 / 34

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  51. CLICS² Ideas
    Ideas
    use the state of the art of available wordlist data
    separate data from display (CLICS² does not host data, but simply
    uses it)
    curate data following the recommendations developed for the
    Cross-Linguistic Data Formats (CLDF, http://cldf.clld.org)
    initiative (Forkel et al. 2017)
    curate the code and the data with help of a transparent API
    regularly release the data in release circles of about 1 per year
    (following the practice of Glottolog and other CLLD projects)
    15 / 34

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  52. CLICS² Excursus
    Excursus: The Cross-Linguistic Data Initiative
    Cross-Linguistic Data Formats (Forkel et al. 2017)
    aims at increasing the comparability of cross-linguistic
    data and analyses
    supports methods for standardization via reference
    catalogues like Glottolog (Hammarström et al. 2018)
    and Concepticon (List et al. 2017)
    provides software APIs which help to test whether data
    conforms to standards
    offers working examples for best practice
    supported by different software frameworks (LingPy,
    BEASTling, EDICTOR)
    16 / 34

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  53. CLICS² Excursus
    CLDFDEMO
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  54. CLICS² Excursus
    Excursus: Reference Catalogues
    The advantages of linking one’s data to reference catalogs like
    Glottolog (Hammarström et al. 2018, http://glottolog.org)
    are obvious: Since Glottolog harvests various types of additional
    information regarding language varieties all over the world that can
    be used effortlessly, once linked.
    18 / 34

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  55. CLICS² Excursus
    Excursus: Reference Catalogues
    The advantages of linking one’s data to reference catalogs like
    Glottolog (Hammarström et al. 2018, http://glottolog.org)
    are obvious: Since Glottolog harvests various types of additional
    information regarding language varieties all over the world that can
    be used effortlessly, once linked.
    The Concepticon project (http://concepticon.clld.org,
    List et al. 2016, List et al. 2018) is much less well known among
    scholars, but it offers the same advantages when dealing with
    wordlist data that was built by means of a questionnaire of
    “elicitation glosses”.
    18 / 34

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  56. CLICS² Excursus
    Excursus: Concepticon
    Concepticon (List et al. 2016)
    link concept labels (“elicitation glosses”) in published
    concept lists (questionnaires) to concept sets
    link concept sets to meta-data
    define relations between concept sets
    never link one concept in a given list to more than one
    concept set (guarantees consistency)
    provide an API to check the consistency of the data and
    to query the data
    provide a web-interface to browse through the data
    19 / 34

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  57. CLICS² Excursus
    Concepticon
    ID Concept in Source English Gloss Conceptlist
    Alpher-1999-151-27 fat, grease [english] Alpher 1999 151
    He-2010-207-145 脂肪 [chinese] fat He 2010 207
    Janhunan-2008-235-96 fat / grease [english] Janhunan 2008 235
    Gudschinsky-1956-200-42 fat-grease [english] Gudschinsky 1956 200
    Swadesh-1952-200-43 fat (organic substance) [english] Swadesh 1952 200
    Swadesh-1955-100-26 fat (grease) [english] Swadesh 1955 100
    ... ... ... ...
    Concept Set FAT (ORGANIC SUBSTANCE)
    Related concept sets
    Esters of three fatty acid chains and the alcohol glycerol which form a semi-solid
    substance in room temperature and occur in animals and plants.
    20 / 34

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  58. CLICS² Excursus
    Concepticon
    English German Chinese French Spanish
    Russian Portuguese
    Selected language: en
    fece|
    MATCH ID GLOSS DEFINITION SIMILARITY
    face 1560 FACE
    The front part of the head, featuring the eyes, nose,
    and mouth and the surrounding area.
    3
    feces 675
    FAECES
    (EXCREMENT)
    Substance that human and animal bodies release from
    time to time as a little pile of waste remaining from
    digestion, after it has been collected in the colon.
    3
    fence 1690 FENCE Delimitation for an area. 3
    20 / 34

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  59. CLICS² Excursus
    CONCEPTICON DEMO
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  60. CLICS² Excursus
    Excursus: Data in CLDF
    # Dataset Source Range Glosses Concepticon Varieties Glottolog Families
    1 allenbai Allen (2007) Bai (ST) 500 499 9 9 1
    2 bantubvd Greenhill & Gray (2015) Bantu 430 415 10 9 1
    3 beidasinitic Běijīng Dàxué (1964) Sinitic (ST) 905 700 18 18 1
    4 bowernpny Bowern & Atkinson (2011) Pama-Nyungan 348 342 171 164 2
    5 hubercolumbian Huber & Reed (1992) Colombian 374 343 69 65 16
    6 ids Key & Comrie (2016) World-wide 1305 1305 324 234 61
    7 kraft Kraft (1981) Chadic 434 428 67 60 3
    8 northeuralex Dellert & Jäger (2017) North-Eurasian 1016 940 107 105 21
    9 robinsonap Robinson & Holton (2012) Alor-Pantar 398 386 13 11 1
    10 satterthwaitetb Satterthwaite-Phillips (2011) Sino-Tibetan 423 418 18 15 1
    11 sunztb Sūn (1991) Sino-Tibetan 1005 906 50 44 1
    12 tls Nurse and Phillipson (1975) Tanzanian 1533 808 131 97 1
    13 tryonsolomon Tryon and Hackman (1983) Solomon Islands 324 311 111 96 5
    14 wold Haspelmath & Tadmor (2009) World-wide 1460 1457 41 40 25
    15 zgraggenmadang Z’graggen (1980abcd) Madang 336 306 100 98 1
    TOTAL / OVERLAP 2482 1266 1036 91
    Datasets are all released under https://zenodo.org/communities/clics.
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  61. CLICS² Excursus
    Excursus: Data in CLDF
    Since our datasets are all available in CLDF format, we can easily
    aggregate them for our new version of CLICS².
    23 / 34

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  62. CLICS² Excursus
    Excursus: Data in CLDF
    Since our datasets are all available in CLDF format, we can easily
    aggregate them for our new version of CLICS².
    Given problems with concept overlap in the datasets, we offer code
    examples that can be used to compute mutual coverage statists
    allowing users to select subsets of the data optimal for a given
    analysis.
    23 / 34

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  63. CLICS² Excursus
    Excursus: Data in CLDF
    average mutual coverage
    300 400 500 600 700 800 900 1000
    language
    0.0
    0.2
    0.4
    0.6
    0.8
    1.0
    2400
    2200
    2000
    1800
    1600
    1400
    1200
    1000
    800
    600
    400
    200
    A B
    60 80 100 120 140 160 180 200 220
    languages
    0.0
    0.2
    0.4
    0.6
    0.8
    1.0
    1280
    1180
    1080
    980
    880
    780
    680
    580
    480
    380
    280
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  64. CLICS² Excursus
    Excursus: Software API
    25 / 34

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  65. CLICS² Excursus
    Excursus: Software API
    With the Python API that we have prepared for CLICS²
    (https://github.com/clics/clics2), users are able to use
    their own data to run their own network analyses. Since all data for
    CLICS² is independently shared and curated, users can also use the
    data we selected for CLICS² but test different parameters of our API.
    25 / 34

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  66. CLICS² Excursus
    Excursus: Software API
    With the Python API that we have prepared for CLICS²
    (https://github.com/clics/clics2), users are able to use
    their own data to run their own network analyses. Since all data for
    CLICS² is independently shared and curated, users can also use the
    data we selected for CLICS² but test different parameters of our API.
    We offer examples of how the data we use for CLICS² can be
    computed with help of the API and plan to make them available in
    form of code cookbooks.
    25 / 34

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  67. CLICS² Excursus
    Excursus: Software API
    With the Python API that we have prepared for CLICS²
    (https://github.com/clics/clics2), users are able to use
    their own data to run their own network analyses. Since all data for
    CLICS² is independently shared and curated, users can also use the
    data we selected for CLICS² but test different parameters of our API.
    We offer examples of how the data we use for CLICS² can be
    computed with help of the API and plan to make them available in
    form of code cookbooks.
    By shifting to the CLLD framework, scholars can also create their
    own CLICS websites, since the source code for the creation of
    interactive networks is transparently shipped with the data.
    25 / 34

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  68. CLICS² Features
    Features: Summary
    26 / 34

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  69. CLICS² Features
    Features: Summary
    drastic increase in data
    26 / 34

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  70. CLICS² Features
    Features: Summary
    drastic increase in data
    drastic increase in transparency
    26 / 34

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  71. CLICS² Features
    Features: Summary
    drastic increase in data
    drastic increase in transparency
    drastic increase in replicability
    26 / 34

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  72. CLICS² Features
    Features: Summary
    drastic increase in data
    drastic increase in transparency
    drastic increase in replicability
    regular floating releases which feature new data
    26 / 34

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  73. CLICS² Features
    Features: Summary
    drastic increase in data
    drastic increase in transparency
    drastic increase in replicability
    regular floating releases which feature new data
    strict and clear-cut collaboration guidelines
    26 / 34

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  74. CLICS² Features
    Features: Summary
    drastic increase in data
    drastic increase in transparency
    drastic increase in replicability
    regular floating releases which feature new data
    strict and clear-cut collaboration guidelines
    new methods (see demo on next slide)
    26 / 34

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  75. CLICS² Features
    Features: Summary
    drastic increase in data
    drastic increase in transparency
    drastic increase in replicability
    regular floating releases which feature new data
    strict and clear-cut collaboration guidelines
    new methods (see demo on next slide)
    rigid policy towards open data (since we heavily profit from all of our
    colleagues who publish their data!)
    26 / 34

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  76. CLICS² Features
    Features: Coverage
    27 / 34

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  77. CLICS² Features
    Features: Enhanced Browsing
    28 / 34

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  78. CLICS² Features
    Features: Enhanced Browsing
    Thanks to the CLLD framework, the data is now much easier to
    browse, and all data is clearly linked to the original datasets.
    28 / 34

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  79. CLICS² Features
    Features: Enhanced Browsing
    Thanks to the CLLD framework, the data is now much easier to
    browse, and all data is clearly linked to the original datasets.
    Thanks to a standalone app that can be created from our data in
    pure HTML format, users can still browse CLICS² data with the old
    look-and-feel, and even use the standalone application to deploy
    their own data in form of CLICS networks.
    In addition, we are currently experimenting with a new visualization
    that allows users to inspect the CLICS² network in all its complexity,
    following visualization methods developed for the inspection of
    Galaxies (contributed by Thomas Mayer).
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  80. CLICS² Features
    Features: Examples
    CARRY IN HAND
    CARRY UNDER ARM
    RULE
    ORDER
    SALT
    TAKE
    CHOOSE
    LEND SHARE
    BRING
    FORGET
    ACQUIT
    HAVE SEX
    HAND
    LIBERATE
    DIRTY
    GUEST
    ARM
    BETWEEN
    UPPER ARM
    MOLD
    TORCH OR LAMP
    OWN
    GAP (DISTANCE)
    DRIP (EMIT LIQUID)
    FINGERNAIL OR TOENAIL
    RIVER
    KISS
    RAIN (PRECIPITATION)
    WHEN
    SPOON
    SUCK
    ROUND
    LICK
    FINGERNAIL
    CLAW SOUP
    DRINK
    FORK
    PITCHFORK
    WATER
    SEA
    OPEN
    SMOKE (INHALE)
    LET GO OR SET FREE
    CAUSE
    DIRT
    FORKED BRANCH
    SEND
    LIP
    FORGIVE
    UNTIE
    ANCHOR
    EAT
    BITE
    BEVERAGE
    SWALLOW
    SAP
    URINE
    ANKLE
    FISHHOOK
    WHEEL
    WHERE
    LIFT
    CHIEFTAIN
    LOWER ARM
    CAUSE TO (LET)
    QUEEN
    GIVE
    ELBOW
    DONATE
    ELECTRICITY
    SKY
    STORM CLOUDS
    MUD
    SWAMP
    SMOKE (EXHAUST)
    FRESH
    SMOKE (EMIT SMOKE)
    STRANGER
    CEASE
    MOORLAND
    HOST
    GO UP (ASCEND)
    WEDDING
    CLIMB
    CLOUD
    PALM OF HAND
    FIVE
    MARRY
    RISE (MOVE UPWARDS)
    WRIST
    KING
    PRESIDENT
    FATHOM
    COLLARBONE
    RIDE
    SPACE (AVAILABLE)
    MASTER
    SHOULDER
    BROOM
    RAKE
    FLESH
    HOOK
    DRIBBLE
    SPIT
    TOE
    PAW
    OCEAN
    FINGER
    LAKE
    EDGE
    OBSCURE
    TOP
    NIGHT
    INCREASE
    WORLD
    UP
    DARKNESS
    BE
    GOD
    CALF OF LEG
    LEG
    SHIN
    FISH
    LOWER LEG
    WOMAN
    FEMALE (OF PERSON)
    FEMALE
    FEMALE (OF ANIMAL)
    LAGOON
    CORNER
    BORDER
    BESIDE
    FRINGE
    BOUNDARY
    WIFE
    COAST
    POINTED
    SHARP
    SHORE
    PLACE (POSITION)
    END (OF SPACE)
    EARTH (SOIL)
    BLACK
    STAND UP
    CHEW
    MEAL
    BREAKFAST
    HEEL
    FOOD
    DINNER (SUPPER)
    FOOT
    STAR
    SAND
    CLAY
    STAND
    SHOULDERBLADE
    CRAWL
    WAKE UP FOG
    FINISH
    DARK
    MALE ICE
    WAIST
    MARRIED MAN
    HIP
    DEEP
    LUNG
    FOAM
    REMAINS
    BLUE
    WAIT (FOR)
    LIFE
    LATE
    BE ALIVE
    AFTER
    TOWN
    BEHIND
    ASH
    FLOUR
    STATE (POLITICS)
    NEW
    UPPER BACK
    BOTTOM
    PASTURE
    THATCH
    BUTTOCKS
    MAN
    MALE (OF ANIMAL)
    MALE (OF PERSON)
    SIT DOWN
    TALL
    CROUCH
    EVENING
    AFTERNOON
    HIGH
    WEST
    GROW
    MAINLAND
    SIT
    LAND
    FLOOR
    AREA
    HALT (STOP)
    DUST
    REMAIN
    GROUND
    NATIVE COUNTRY
    DWELL (LIVE, RESIDE)
    COUNTRY
    HUSBAND
    BACK
    END (OF TIME)
    SPINE
    GRASS
    DEW
    MARRIED WOMAN
    ROOSTER
    INSECT
    FOWL
    BIRD
    ANIMAL
    HEN
    SHORT
    BABY
    CORN FIELD
    THIN
    SAGO PALM
    GARDEN
    SMALL
    THIN (OF SHAPE OF OBJECT)
    CLAN
    NARROW
    FAMILY
    YOUNG
    CITIZEN
    FINE OR THIN
    SHALLOW
    THIN (SLIM)
    GIRL
    RELATIVES
    YOUNG MAN
    FRIEND
    PARENTS
    CHILD (DESCENDANT)
    YOUNG WOMAN
    BOY
    NEIGHBOUR
    CHILD (YOUNG HUMAN)
    SON
    SIBLING
    BROTHER
    DESCENDANTS
    OLDER SIBLING
    DAUGHTER
    ALONE
    FENCE
    ONLY
    FEW
    TOWER
    SOME
    ONE
    YARD
    OUTSIDE
    FORTRESS
    NEVER
    PLAIN
    PEOPLE
    VALLEY
    DOWN
    FIELD
    LOW
    PERSON
    YOUNGER SIBLING
    YOUNGER SISTER
    OLDER BROTHER
    YOUNGER BROTHER
    COUSIN
    SISTER
    OLDER SISTER
    NEPHEW
    DAMP
    FLOWER
    MANY
    SMOOTH
    WIDE
    FLAT
    BLOOD
    WET
    BELOW OR UNDER
    DOWN OR BELOW
    GREY
    BREAD
    DOUGH
    RAW
    VILLAGE
    GREEN
    CROWD
    SOFT
    AT
    ALL
    SLIP
    UNRIPE
    VEIN
    BLOOD VESSEL
    ALWAYS
    TENDON
    ROOF
    ROOT
    INSIDE
    OR
    GENTLE
    OLD
    WITH
    ENOUGH
    OLD (AGED)
    FORMER
    AND
    ROOM
    HOME
    TENT
    HUT
    GARDEN-HOUSE
    WEAK
    DENSE
    MEN'S HOUSE
    OLD MAN
    LAZY
    STILL (CONTINUING)
    TIRED
    AGAIN
    MORE
    READY
    OLD WOMAN
    SOMETIMES
    IN
    HOUSE
    OFTEN
    YELLOW
    RED
    AFTERWARDS
    BIG
    GOLD
    YOLK
    HOUR
    SALTY
    PINCH
    KNEEL
    AGE
    RIPE
    THICK
    FULL
    STRAIGHT
    BE LATE
    LIGHT (RADIATION) ABOVE
    WORK (ACTIVITY)
    PRODUCE
    MAKE
    DAY (NOT NIGHT)
    HEAVEN
    WORK (LABOUR) BUILD
    FAR
    AT THAT TIME
    LONG
    WHITE
    LENGTH
    THEN
    MOUNTAIN OR HILL
    SEASON
    HAVE
    PRESS
    GET
    PICK UP
    HEAD
    HOLD
    EARN
    DO OR MAKE
    WEATHER
    FATHER
    STEPFATHER
    UNCLE
    FATHER-IN-LAW (OF MAN)
    FATHER'S BROTHER
    MOTHER'S BROTHER
    STEPMOTHER
    AUNT
    BEGINNING
    BEGIN
    FIRST
    FATHER'S SISTER
    MOTHER-IN-LAW (OF WOMAN)
    MOTHER'S SISTER
    MOTHER
    MOTHER-IN-LAW (OF MAN)
    PARENTS-IN-LAW
    GRANDDAUGHTER
    SON-IN-LAW (OF WOMAN)
    FATHER-IN-LAW (OF WOMAN)
    SON-IN-LAW (OF MAN)
    DAUGHTER-IN-LAW (OF WOMAN)
    CHILD-IN-LAW
    SIBLING'S CHILD
    NIECE
    GRANDFATHER
    DAUGHTER-IN-LAW (OF MAN)
    IN FRONT OF
    FORWARD
    GRANDSON
    GRANDCHILD
    GRANDMOTHER
    ANCESTORS
    GRANDPARENTS
    THING
    STREET
    MANNER
    ROAD
    PIECE
    PORT
    PATH OR ROAD
    PATH
    RIB
    BONE
    BAIT
    THIGH
    BAY
    FLESH OR MEAT MEAT FOOTPRINT
    SIDE
    PART
    SLICE
    WALL (OF HOUSE)
    MIDDLE
    NAVEL
    SNOW
    LAST (FINAL)
    HAY HALF
    NEAR
    CHICKEN
    BULL
    SNAKE
    WORM
    CATTLE
    LIVESTOCK
    CALF
    OX
    COW
    WHICH
    WHITHER (WHERE TO)
    WINE
    HOW
    CIRCLE
    RING
    BALL
    BRACELET
    HOW MUCH
    HOW MANY
    BEEHIVE
    GRAVE
    CAVE
    BEARD
    RAIN (RAINING)
    SPRING OR WELL
    MOUSTACHE
    STREAM
    GLUE
    ALCOHOL (FERMENTED DRINK)
    BEE
    BEER
    HONEY
    WHO WASP
    MEAD
    WHAT
    WHY
    CANDY
    LUNCH
    ITEM
    WARE
    CUSTOM
    LAW
    MIDDAY
    PIT (POTHOLE)
    HOLE
    FURROW
    DITCH
    LAIR
    JUDGMENT
    COURT
    ADJUDICATE
    CONDEMN
    CONVICT
    ACCUSE
    BLAME
    ANNOUNCE
    PREACH
    EXPLAIN
    SAY
    ASK (REQUEST)
    THROW
    BUDGE (ONESELF)
    SHOOT
    EMBERS
    UGLY
    CHOP
    CUT DOWN
    COLD (OF WEATHER)
    FIREWOOD
    GRASP
    LEAD (GUIDE)
    DISTANCE
    LIE DOWN
    CARRY ON HEAD
    PERMIT
    PUSH
    MOLAR TOOTH
    FRONT TOOTH (INCISOR)
    RIDGEPOLE
    BEAK
    COAT
    TOWEL
    HELMET
    SHIRT
    HEADBAND
    HEADGEAR
    RAG
    VEIL
    SOON
    TOGETHER
    IMMEDIATELY
    NEST
    NOW
    BED
    TODAY
    INSTANTLY
    SUDDENLY
    RUG
    WITHOUT
    PONCHO
    BLANKET
    CLOAK
    MAT
    BEFORE
    BOLT (MOVE IN HASTE)
    ROAR (OF SEA)
    FAST
    DASH (OF VEHICLE)
    EARLY
    YESTERDAY
    HURRY
    AT FIRST
    EMPTY
    NO
    DRY
    ZERO
    NOTHING
    NOT
    RESULT IN
    BE BORN
    HAPPEN
    PASS
    SUCCEED
    BECOME
    BRAVE
    CLOTH
    POWERFUL
    DARE
    LOUD
    GRASS-SKIRT
    DRESS
    CLOTHES
    SKIRT
    RIPEN
    SOLID
    PIERCE
    HARD
    BEGET
    ROUGH
    REFUSE
    FRY
    DRESS UP
    DENY
    CALM
    MORNING
    PEACE
    BE SILENT
    QUIET
    SWELL
    TOMORROW
    HEALTHY
    EXPENSIVE
    HAPPY
    ROAST OR FRY
    STRONG BAKE
    PRICE
    BOIL (SOMETHING)
    PUT ON
    COOKED
    SLOW
    FAITHFUL
    RIGHT
    LAST (ENDURE)
    FOR A LONG TIME
    DAWN
    BEAUTIFUL
    GOOD
    COOK (SOMETHING)
    YES
    CORRECT (RIGHT)
    BOIL (OF LIQUID)
    DO
    PUT
    BRIGHT
    CLEAN
    LIGHT (COLOR)
    LAY (VERB)
    SHINE
    SEAT (SOMEBODY)
    INNOCENT
    FORBID
    PREPARE
    CERTAIN
    TRUTH TRUE
    DEAR
    PRECIOUS
    WARM
    HEAT
    CONCEIVE
    SEW
    LOOM
    PLAIT
    LIGHT (IGNITE)
    BURN (SOMETHING) PREVENT
    HOLY
    GOOD-LOOKING
    ARSON
    BEND
    CHANGE (BECOME DIFFERENT)
    BURNING
    TWIST
    DEBT
    CROOKED
    ROLL
    SPIN
    HEAVY
    HOT
    WEAVE
    DIFFICULT
    FEVER
    PLAIT OR BRAID OR WEAVE
    PREGNANT
    OWE
    TWINKLE
    CLEAR
    BEND (SOMETHING)
    MORTAR CRUSHER
    PESTLE
    BITTER
    MILL MONTH SKULL
    MEASURE
    TRY
    COME BACK TIME
    MOON
    COUNT
    JOIN
    SQUEEZE
    PILE UP
    CLOCK
    BUY
    DRAW MILK
    DAY (24 HOURS)
    BETRAY
    GUARD
    PROTECT
    PAY
    KNEE
    KEEP
    SELL
    SUN
    BILL
    HELP
    LIE (MISLEAD)
    TRADE OR BARTER
    DECEIT
    PERJURY
    RESCUE
    CURE
    FOLD
    SIEVE
    PRESERVE
    TRANSLATE
    TURN (SOMETHING)
    TURN
    WRAP
    HERD (SOMETHING)
    WAGES
    DEFEND
    CHANGE
    RETURN HOME
    TIE UP (TETHER)
    TURN AROUND
    HANG
    KNIT
    WEIGH
    HANG UP
    GIVE BACK
    CONNECT
    COVER
    BUTTON
    BUNCH
    KNOT
    SHUT
    BUNDLE
    TIE
    NOOSE
    GILL
    EAR
    EARLOBE
    THINK
    FOLLOW
    JEWEL
    BE ABLE
    OBEY
    SUMMER
    FEEL (TACTUALLY)
    REMEMBER
    SUSPECT
    BELIEVE
    GUESS
    RECOGNIZE (SOMEBODY)
    SOUR
    SWEET
    SUGAR CANE
    BRACKISH
    SUGAR
    TASTY
    CALCULATE
    IMITATE
    CITRUS FRUIT
    TASTE (SOMETHING)
    READ
    COME
    PRECIPICE
    SEE
    STONE OR ROCK
    APPROACH
    TOUCH
    ARRIVE
    YEAR
    MEET
    GRIND
    FRAGRANT
    ROTTEN SMELL (STINK)
    SMELL (PERCEIVE)
    STINKING
    SNIFF
    PUS
    FEEL
    UNDERSTAND
    HEAR
    THINK (BELIEVE)
    LISTEN
    MOVE (AFFECT EMOTIONALLY)
    KNOW (SOMETHING)
    NOTICE (SOMETHING)
    WATCH
    LEARN
    REEF
    STUDY
    LOOK FOR
    LOOK
    NASAL MUCUS (SNOT)
    SPLASH
    PITY
    HIDE (CONCEAL)
    SHELF
    FLY (MOVE THROUGH AIR)
    REGRET
    NOSTRIL
    THIEF
    BOARD
    SINK (DESCEND)
    DECREASE
    CHEEK
    NOSE
    BROKEN
    LOSE
    EMERGE (APPEAR)
    ANXIETY
    BAD LUCK
    GOOD LUCK
    OMEN
    WRONG
    SLAB
    FOREHEAD
    EYE
    BAD
    EVIL
    TABLE
    INJURE
    DANGER
    SURPRISED
    HARVEST
    BERRY
    FEAR (FRIGHT)
    NUT FAULT
    MISTAKE
    BECOME SICK
    SEED
    MISS (A TARGET)
    GUILTY
    SWELLING
    BRUISE
    BLISTER
    BOIL (OF SKIN)
    SCAR
    CHOKE
    ENTER
    ACHE
    SICK
    DISEASE
    PAIN
    DAMAGE (INJURY)
    SEVERE
    GRIEF
    SAUSAGE
    BEAD
    STOMACH
    INTESTINES
    CHAIN
    SPLEEN
    NECKLACE
    WOMB
    LIVER
    BELLY
    MEANING
    GHOST
    POSTCARD
    HEART
    LEGENDARY CREATURE
    SHADE
    DEMON
    BRAIN MEMORY
    FIGHT
    LETTER
    THOUGHT
    MIND
    BOOK
    COLLAR INTENTION
    SPIRIT
    PURSUE
    LONG HAIR
    SPRINGTIME
    HAIR (HEAD)
    THINK (REFLECT)
    DOUBT
    AUTUMN
    ORNAMENT
    HOPE
    ARMY
    QUARREL
    BEAT
    SOLDIER
    KNOCK
    BATTLE
    NOISE
    REST
    NAPE (OF NECK)
    THROAT
    NECK
    IDEA
    IF
    BECAUSE
    SLEEP
    FOREST
    DRIP (FALL IN GLOBULES)
    STICK
    TREE
    WALKING STICK
    PLANT (VEGETATION)
    LIE (REST)
    DRAG
    ASK (INQUIRE)
    DIVIDE
    URGE (SOMEONE)
    STING
    BRANCH
    CAMPFIRE BORROW SEPARATE TOOTH
    MOUTH
    CANDLE
    FALL ASLEEP
    DRIVE (CATTLE)
    MATCH
    DRIVE
    RAFTER
    BEAM
    DOORPOST
    DREAM (SOMETHING)
    POST
    MAST
    TUMBLE (FALL DOWN)
    WALK
    TREE TRUNK
    LAND (DESCEND)
    TEAR (SHRED)
    SAW
    GO OUT
    FALL
    TEAR (OF EYE) GO DOWN (DESCEND)
    BODY
    TREE STUMP
    SHOW
    CARVE
    SPOIL (SOMEBODY OR
    SOMETHING)
    BREAK (CLEAVE)
    PLANT (SOMETHING)
    DESTROY
    WALK (TAKE A WALK)
    CHIN
    BREAK (DESTROY OR GET
    DESTROYED)
    CUT
    PICK
    SPLIT
    LEAVE
    PULL
    CLUB
    WOOD
    MOVE (ONESELF)
    HIRE
    PRAISE
    MIX
    KNEAD
    WIPE
    SNEEZE
    BOAST
    SCRATCH
    CLEAN (SOMETHING)
    HOARFROST
    WORSHIP
    COUGH
    SWEEP
    RUB
    SCRAPE
    CARCASS
    DIE (FROM ACCIDENT)
    DIE
    BATHE
    SWIM
    DEAD
    FLOAT
    LOVE
    STAB
    SAIL
    PEEL
    SPREAD OUT
    CRY
    COMMON COLD (DISEASE)
    FROST
    CORPSE
    SHRIEK
    JUMP
    SHOUT
    DIG
    WINTER
    NAME
    STREAM (FLOW CONTINUOUSLY)
    PLOUGH
    CULTIVATE
    PLAY
    VISIBLE
    SEEM
    STRETCH
    SOW SEEDS
    RETREAT
    INVITE
    MUSIC
    RUN
    COLD
    HOLLOW OUT
    CHARCOAL
    TONGUE
    STOVE
    CONVERSATION
    SKIN
    DIVORCE
    OVEN
    EARWAX
    COOKHOUSE
    TIP (OF TONGUE)
    AIR
    HUNT
    BORE
    CALL BY NAME
    BREATH
    STEP (VERB)
    SONG
    ATTACK
    WASH
    PROUD
    SIN
    DEFENDANT
    CRIME
    CHIME (ACTION) EGG
    TESTICLES
    BARLEY
    FRUIT
    VEGETABLES
    GRAIN
    MAIZE
    RICE
    WHEAT
    RUDDER
    RYE
    PADDLE SWAY
    SWING (MOVEMENT)
    SWING (SOMETHING)
    SHAKE
    ROW
    FREEZE
    JOG (SOMETHING)
    OAT
    SHIVER
    RINSE
    RING (MAKE SOUND)
    MAKE NOISE
    SOUND (OF INSTRUMENT OR
    VOICE)
    TINKLE
    HOE
    SHOVEL
    SPADE
    FLOW
    DANCE
    FLEE
    CALL
    DAMAGE
    SAME FACE
    SIMILAR DISAPPEAR
    ESCAPE
    PRAY GAME
    BURY
    CAPE
    CHAIR
    MOVE
    STEAL
    GROAN
    HOWL
    COLD (CHILL)
    JAW
    DROWN
    SINK (DISAPPEAR IN WATER)
    SET (HEAVENLY BODIES)
    DIVE
    WOUND
    POUND
    TALK
    BREATHE
    PROMISE
    SPEAK
    WIND
    VOICE
    FUR
    PUBIC HAIR
    SOUND OR NOISE
    STRIKE OR BEAT
    BARK
    SCALE
    KILL
    HAMMER
    TONE (MUSIC)
    WOOL
    EXTINGUISH
    MURDER
    HIT
    SPEECH
    CHAT (WITH SOMEBODY)
    WORD
    STORM
    THRESH
    LEATHER
    LIKE
    NEED (NOUN)
    FELT
    SKIN (OF FRUIT)
    PAPER
    OATH
    WANT
    SWEAR
    KICK
    SNAIL
    DEATH
    PULL OFF (SKIN)
    SHELL
    FIREPLACE
    PEN
    HAIR (BODY)
    LANGUAGE
    CONVEY (A MESSAGE)
    TELL
    LEAF (LEAFLIKE OBJECT)
    FEATHER
    POUR
    FLAME
    GO
    SING
    BEESWAX
    HELL
    GATHER
    CARRY
    SEIZE
    CATCH
    TRAP (CATCH)
    WING
    FIRE
    CARRY ON SHOULDER
    CAST
    MOW
    BOSS
    FIND
    FIN
    ADMIT
    TEACH
    LEAF
    SAILCLOTH
    HAIR ANSWER
    SAY
    FOOT
    CIRCLE
    GRAIN
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  81. CLICS² Features
    Features: Examples
    TONGUE
    TELL ANNOUNCE
    TALK
    TIP (OF TONGUE)
    ADMIT
    CHAT (WITH SOMEBODY)
    SAY
    WORD
    ANSWER
    LANGUAGE
    VOICE
    SOUND OR NOISE
    NOISE
    PREACH
    SPEECH
    TONE (MUSIC)
    EXPLAIN
    CONVERSATION
    CONVEY (A MESSAGE)
    SPEAK
    29 / 34

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  82. CLICS² Features
    Features: Examples
    TOE
    ANKLE
    ROUND
    RING
    FOOT
    CIRCLE
    WHEEL
    LEG
    BALL
    FOOTPRINT
    HEEL
    29 / 34

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  83. CLICS² Features
    Features: Examples
    29 / 34

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  84. CLICS² Features
    CLICS² DEMO
    30 / 34

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  85. CLICS² Schedule
    Schedule
    31 / 34

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  86. CLICS² Schedule
    Schedule
    CLICS data is currently being released, see
    https://zenodo.org/communities/clics.
    31 / 34

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  87. CLICS² Schedule
    Schedule
    CLICS data is currently being released, see
    https://zenodo.org/communities/clics.
    CLICS² is deployed online in a beta-version (0.1) at
    http://clics.clld.org and published by List, Greenhill,
    Anderson, Mayer, Tresoldi and Forkel (2018).
    31 / 34

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  88. CLICS² Schedule
    Schedule
    CLICS data is currently being released, see
    https://zenodo.org/communities/clics.
    CLICS² is deployed online in a beta-version (0.1) at
    http://clics.clld.org and published by List, Greenhill,
    Anderson, Mayer, Tresoldi and Forkel (2018).
    The official version will be published along with our paper on CLICS²
    (List et al. forthcoming, Linguistic Typology), approximately by the
    end of July.
    31 / 34

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  89. CLICS² Schedule
    Schedule
    CLICS data is currently being released, see
    https://zenodo.org/communities/clics.
    CLICS² is deployed online in a beta-version (0.1) at
    http://clics.clld.org and published by List, Greenhill,
    Anderson, Mayer, Tresoldi and Forkel (2018).
    The official version will be published along with our paper on CLICS²
    (List et al. forthcoming, Linguistic Typology), approximately by the
    end of July.
    The space-ship visualization will be deployed online later this year.
    31 / 34

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  90. Outlook
    Outlook
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  91. With CLICS², we provide a new framework for the
    collection and curation of data for the purpose of
    studying cross-linguistic colexification patterns.
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  92. With CLICS², we provide a new framework for the
    collection and curation of data for the purpose of
    studying cross-linguistic colexification patterns.
    Future updates are planned, and we assume that we
    will be able to increase the data further by at least five
    more larger datasets.
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  93. With CLICS², we provide a new framework for the
    collection and curation of data for the purpose of
    studying cross-linguistic colexification patterns.
    Future updates are planned, and we assume that we
    will be able to increase the data further by at least five
    more larger datasets.
    CLICS² is not perfect, and it does not come with any
    warranty. However, we hope that the improvements in
    terms of data transparency will make it much easier for
    scholars to work with the new cross-linguistic
    colexification database than its predecessor.
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  94. Thanks to our CLICS² team:
    Simon Greenhill, Cormac Anderson, Thomas Mayer, Tiago Tresoldi, and
    Robert Forkel
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  95. Thanks to our CLICS² team:
    Simon Greenhill, Cormac Anderson, Thomas Mayer, Tiago Tresoldi, and
    Robert Forkel
    Thank You for your attention!
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