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CLICS2 : An improved database and a computer-assisted framework for cross-linguistic colexifications

CLICS2 : An improved database and a computer-assisted framework for cross-linguistic colexifications

Presentation held during the ""Lexical semantics, language change, and universal translators" workgroup at the Santa Fe Institute.

Tiago Tresoldi

August 27, 2018

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  1. CLICS2 : An improved database and a computer-assisted framework for

    cross-linguistic colexifications Tiago Tresoldi Computer-Assisted Language Comparison (CALC) Max Planck Institute for the Science of Human History (MPI-SHH / JENA) Santa Fe, 2018-08-27
  2. 2 Colexifications? • A colexification, building on Haspelmath (2003) and

    François (2008), occurs when, in a same language, two different concepts are expressed by the same word. • Agnostic term for covering homophony and polysemy
  3. 3 Early Accounts: People and Ideas • Haspelmath (2003). The

    geometry of grammatical meaning. • François (2008). Semantic maps and the typology of colexifications. • Cysouw (2010). Drawing networks from recurrent polysemies. • Steiner, Stadler, and Cysouw (2011). A pipeline for computational historical linguistics. • Urban (2011). Asymmetries in overt marking and directionality in semantic change.
  4. 4 Techniques and Ideas • Cysouw (2010) uses polysemy data

    to draw networks • Mayer, List, Terhalle, and Urban (2014) develop an interactive way to visualize cross-linguistic colexification • List, Mayer, Terhalle, and Urban (2014) publish the database and the web-application under the name CLICS (Database of Cross-Linguistic Colexifications) • In contrast to earlier single-database attempts, multiple datasets are merged • The community detection procedure is improved by using Infomap (Rosvall and Bergstrom, 2008), an algorithm based on random walks in complex networks
  5. 5 CLICS1: Data • IDS (Key and Comrie, 2007) •

    233 languages of which 178 were included in CLICS • WOLD (Haspelmath and Tadmor, 2009) • 41 languages of which 33 were included in CLICS • Logos Dictionary (Logos Group) • 60 languages, of which 4 were included in CLICS • Språkbanken project (University of Gothenburg) • 8 SEA languages, of which 6 were included in CLICS
  6. 6 CLICS1: Methods • Problem A: Data cannot be displayed

    fully, complexity needs to be reduced • Show communities instead of showing all the data • Subgraph-view that cuts out the nearest neighbors of one concept to compensate for data loss in the community view • Problem B: Data is noisy and needs to be corrected • Filter by language families and weight the concept links by frequency of occurrence, following the suggestion of Dellert (2014) • This cuts most of the links from homophony, filtering for polysemy
  7. 7 CLICS1: Interface • Backend in PHP, frontend in Javascript

    • Transparency and reproducibility • The underlying network with the inferred communities can be downloaded from the website • The entire code for analysis and visualization is available on GitHub • The complete set of wordlists is available from Zenodo
  8. 9 CLICS2: Problems of CLICS1 • Difficult to curate (error

    correction, data extension) • Difficult to expand • Difficult do collaborate • Difficult to communicate • Not all users understand that data was aggregated and not collected by the authors, and that corrections are to be considered new, derived datasets • Difficult to catch up • Best practices of data curation were learned while developing CLICS, but those were difficult to integrate in the workflow
  9. 10 CLICS2: Ideas • Use the state-of-the-art of available wordlist

    data • Separate data from display • CLICS2 does not host data, but uses it • Curate data following the recommendations developed for the Cross-Linguistic Data Formats (CLDF) initiative (Forkel et al., 2017) • Curate the code and the data with the help of a transparent API • Regularly release the data in release circles of about once a year • Practice of Glottolog and other CLLD projects
  10. 11 Excursus: CLDF • Aims at increasing the comparability of

    cross-linguistic data and analyses • Supports methods for standardization via reference catalogs 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 practices • Supported by different software frameworks (LingPy, BEASTling, EDICTOR)
  11. 13 Excursus: Reference Catalogs • Linking to Glottolog is advantageous,

    as it harvests various types of additional information regarding languages, all of which can be used effortlessly • The Concepticon project (List et al., 2016, List et al., 2018) is less known, but offers the same advantages when dealing with wordlists built by means of “elicitation glosses”
  12. 14 Excursus: Concepticon • 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
  13. 17 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 statistics allowing users to select subsets of the data optimal for a given analysis
  14. 20 CLICS2: Software API • By using the Python API,

    users are able to use their own data and run their own network analyses • Since all the data for CLICS2 is independently shared and curated, users can also use the data with different parameters • We offer examples on how the CLICS2 data can be computed with the help of the API • By shifting to the CLDF framework, scholars can also create their own CLICS-like websites, since the source code for creation of interactive networks is transparently shipped with the data
  15. 21 CLICS2: Features • 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 • Rigid policy towards open data • Since we heavily profit from all our collegues who publish their data!
  16. 22 CLICS2: Enhanced Browsing • Thanks to the CLLD framework,

    the data is now easier to browse and clearly linked to the original datasets • Thanks to a standalone app that can be created, users can browse CLICS2 data with the CLICS1 look-and-feel and even deploy their own version • We are currently experimenting with new visualizations • Showcase: visualization methods developed for the inspection of galaxies (contributed by Thomas Mayer) https://bit.ly/2PaZmxx or
  17. 26 Schedule • CLICS2 data is currently being released, see

    https://zenodo.org/communities/clics • CLICS2 is deployed online at http://clics.clld.org and published by List, Greenhill, Anderson, Mayer, Tresoldi, and Forkel (2018) • The spaceship visualization will be deployed online later this year
  18. 27 Outlook • 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 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 • CLICS2 is our showcase product to have people jumping on board of the CLDF initiative
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