CLICS2: An improved database of cross-linguistic colexifications assembling lexical data with the help of cross-linguistic data formats

List, Johann‐Mattis; Greenhill, Simon J.; Anderson, Cormac; Mayer, Thomas; Tresoldi, Tiago; Forkel, Robert

doi:10.1515/lingty-2018-0010

Cited by 47 publications

(53 citation statements)

References 26 publications

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“…Once the database has been generated, a colexification graph can be computed. As already described when introducing CLICS 18 and CLICS2 34 , a colexification graph is an undirected graph in which nodes represent comparable concepts and edges express the colexification weight between the concepts they link: for example, wood and tree, two concepts that as already mentioned colexify in many languages, will have a high edge weight, while water and dog, two concepts without a single instance of lexical identity in our data, will have an edge weight of zero.…”

Section: Background and Summarymentioning

confidence: 73%

The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies

et al. 2020

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Advances in computer-assisted linguistic research have been greatly influential in reshaping linguistic research. With the increasing availability of interconnected datasets created and curated by researchers, more and more interwoven questions can now be investigated. Such advances, however, are bringing high requirements in terms of rigorousness for preparing and curating datasets. Here we present CLICS, a Database of Cross-Linguistic Colexifications (CLICS). CLICS tackles interconnected interdisciplinary research questions about the colexification of words across semantic categories in the world's languages, and show-cases best practices for preparing data for cross-linguistic research. This is done by addressing shortcomings of an earlier version of the database, CLICS2, and by supplying an updated version with CLICS3, which massively increases the size and scope of the project. We provide tools and guidelines for this purpose and discuss insights resulting from organizing student tasks for database updates.

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Section: Background and Summarymentioning

confidence: 73%

The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies

et al. 2020

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“…Colexification represents another phenomenon from comparative linguistics with psychological implications. Colexification occurs when two concepts are expressed with a single word (Francois, 2008;List et al, 2018). For example, the English word "funny" colexifies the concepts of humorous and odd whereas the Russian word "ruka" colexifies arm and hand.…”

Section: Comparative Linguistics As a Way To Understand Cultural Divementioning

confidence: 99%

From Text to Thought: How Analyzing Language Can Advance Psychological Science

Jackson¹,

Watts²,

List³

et al. 2020

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Humans have been using language for thousands of years, but psychologists seldom consider what natural language can tell us about the mind. Here we propose that language offers a unique window into human cognition. After briefly summarizing the legacy of language analyses in psychological science, we show how methodological advances have made these analyses more feasible and insightful than ever before. In particular, we describe how two forms of language analysis—comparative linguistics and natural language processing—are already contributing to how we understand emotion, creativity, and religion, and overcoming methodological obstacles related to statistical power and culturally diverse samples. We summarize resources for learning both of these methods, and highlight the best way to combine language analysis techniques with behavioral paradigms. Applying language analysis to large-scale and cross-cultural datasets promises to provide major breakthroughs in psychological science.

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“…This algorithm, which checks a given elicitation gloss against previous manual mappings, works surprisingly well, can currently be carried out in 30 languages, and is provided along with the pyconcepticon Python package which also allows to test the data for internal consistency (Forkel, Rzymski, & List, 2019). For individual concepts, users can consult a web-based lookup tool which offers a slightly simplified mapping algorithm that supports currently seven languages (List et al, 2018). In addition, users who want to contribute can consult tutorials for different levels of expertise (Tjuka, 2020;Tresoldi, 2019).…”

Section: Manual Concept Mappingmentioning

confidence: 99%

Linking Norms, Ratings, and Relations of Words and Concepts Across Multiple Language Varieties

Tjuka¹,

Forkel²,

List³

2020

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Psychologists and linguists have collected a great diversity of data for word and concept properties. In psychology, many studies accumulate norms and ratings such as word frequencies or age-of-acquisition often for a large number of words. Linguistics, on the other hand, provides valuable insights into relations of word meanings. We present a collection of those data sets for norms, ratings, and relations that cover different languages: ‘NoRaRe.’ To enable a comparison between the diverse data types, we established workflows that facilitate the expansion of the database. A web application allows convenient access to the data (https://digling.org/norare/). Furthermore, a software API ensures consistent data curation by providing tests to validate the data sets. The NoRaRe collection is linked to the database curated by the Concepticon project (https://concepticon.clld.org) which offers a reference catalog of unified concept sets. The link between words in the data sets and the Concepticon concept sets makes a cross-linguistic comparison possible. In three case studies, we test the validity of our approach, the accuracy of our workflow, and the applicability of our database. The results indicate that the NoRaRe database can be applied for the study of word properties across multiple languages. The data can be used by psychologists and linguists to benefit from the knowledge rooted in both research disciplines.

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CLICS2: An improved database of cross-linguistic colexifications assembling lexical data with the help of cross-linguistic data formats

Cited by 47 publications

References 26 publications

The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies

The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies

From Text to Thought: How Analyzing Language Can Advance Psychological Science

Linking Norms, Ratings, and Relations of Words and Concepts Across Multiple Language Varieties

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