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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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From Semantic Maps to Cross-Linguistic Polysemy Networks
From Semantic Maps...
... to Cross-Linguistic Polysemies
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From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
Early Accounts: People and Ideas
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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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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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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.
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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.
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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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From Semantic Maps to Cross-Linguistic Polysemy Networks Early Accounts
Early Accounts: Data
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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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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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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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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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From Semantic Maps to Cross-Linguistic Polysemy Networks Initial Ideas
Initial Ideas
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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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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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From Semantic Maps to Cross-Linguistic Polysemy Networks Further Ideas
Further Ideas
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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.
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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).
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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.
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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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CLICS 1.0
CLICS 1.0
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CLICS 1.0 Data
Data
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CLICS 1.0 Data
Data
IDS (Key and Comrie 2007 version), of 233 language varieties, 178
included in CLICS.
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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.
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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.
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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.
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CLICS 1.0 Methods
Methods
Problems
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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.
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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
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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.
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CLICS 1.0 Interface
Interface
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CLICS 1.0 Interface
Interface
Interface is written in JavaScript for the visualizations and PhP for
querying the data.
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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).
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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.
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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).
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CLICS 1.0 Interface
CLICS DEMO
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CLICS²
CLICS²
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CLICS² Motivation
Motivation
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CLICS² Motivation
Motivation
Problems in CLICS 1.0
difficult to curate (error-correction, linking data, adding data)
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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
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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)
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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)
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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)
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CLICS² Ideas
Ideas
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CLICS² Ideas
Ideas
use the state of the art of available wordlist data
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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)
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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)
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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
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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)
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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)
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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)
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CLICS² Excursus
CLDFDEMO
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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.
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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”.
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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
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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.
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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
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CLICS² Excursus
CONCEPTICON DEMO
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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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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.
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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.
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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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CLICS² Excursus
Excursus: Software API
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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.
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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.
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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.
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CLICS² Features
Features: Summary
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CLICS² Features
Features: Summary
drastic increase in data
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CLICS² Features
Features: Summary
drastic increase in data
drastic increase in transparency
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CLICS² Features
Features: Summary
drastic increase in data
drastic increase in transparency
drastic increase in replicability
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CLICS² Features
Features: Summary
drastic increase in data
drastic increase in transparency
drastic increase in replicability
regular floating releases which feature new data
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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
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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)
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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!)
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CLICS² Features
Features: Coverage
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CLICS² Features
Features: Enhanced Browsing
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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.
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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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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
29 / 34
Slide 81
Slide 81 text
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
Slide 82
Slide 82 text
CLICS² Features
Features: Examples
TOE
ANKLE
ROUND
RING
FOOT
CIRCLE
WHEEL
LEG
BALL
FOOTPRINT
HEEL
29 / 34
Slide 83
Slide 83 text
CLICS² Features
Features: Examples
29 / 34
Slide 84
Slide 84 text
CLICS² Features
CLICS² DEMO
30 / 34
Slide 85
Slide 85 text
CLICS² Schedule
Schedule
31 / 34
Slide 86
Slide 86 text
CLICS² Schedule
Schedule
CLICS data is currently being released, see
https://zenodo.org/communities/clics.
31 / 34
Slide 87
Slide 87 text
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
Slide 88
Slide 88 text
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
Slide 89
Slide 89 text
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
Slide 90
Slide 90 text
Outlook
Outlook
32 / 34
Slide 91
Slide 91 text
With CLICS², we provide a new framework for the
collection and curation of data for the purpose of
studying cross-linguistic colexification patterns.
33 / 34
Slide 92
Slide 92 text
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.
33 / 34
Slide 93
Slide 93 text
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.
33 / 34
Slide 94
Slide 94 text
Thanks to our CLICS² team:
Simon Greenhill, Cormac Anderson, Thomas Mayer, Tiago Tresoldi, and
Robert Forkel
34 / 34
Slide 95
Slide 95 text
Thanks to our CLICS² team:
Simon Greenhill, Cormac Anderson, Thomas Mayer, Tiago Tresoldi, and
Robert Forkel
Thank You for your attention!
34 / 34