Author Archives: Annika Tjuka

About Annika Tjuka

My main goal is to answer questions about linguistic diversity with a focus on language variation in word meanings. I have a BA and MA degree in linguistics from the Humboldt University Berlin and am currently pursuing a doctorate at the University of Jena. I am a language scientist studying patterns and causes of words with multiple meanings. For my work, I use data from language documentation, large-scale databases, and computational methods. In my Master’s thesis, I conducted the first systematic study of body part extensions such as table leg and foot of the mountain. I examined the frequency of 95 expressions in 13 languages and the preferences for underlying analogy patterns based on similarity in shape, spatial orientation, and function. The first project of my doctoral research established the Database of Cross-Linguistic Norms, Ratings, and Relations for Words and Concepts (NoRaRe), which contains 98 datasets from linguistics and psychology across 40 languages including 65 word properties.

A list of 171 body part concepts

The body of most human beings consist of similar parts such as a head, arms, legs, and so on. Many body parts also occur in animals. The shapes and functions of body parts are universal across cultures, but speakers of various languages choose to categorize the body differently. For example, Vietnamese has a single word (tay) for the concepts HAND and ARM. The universality of the human body and its categorization into different parts have attracted attention across research areas such as lexical typology and cognitive science. Therefore, I present a comprehensive list of human and animal body part terms based on German which were mapped to the concepts in the Concepticon (List et al. 2020). The list is intended for investigations on cross-linugistic naming patterns of body parts.

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Adding concept lists to Concepticon: A guide for beginners

Scientific data should be openly accessible. This includes databases which are designed for collaborative work. However, in most cases, these databases are only extended by a team of experts. If a database is truly collaborative, the workflows need to be accessible for everybody. The Concepticon database  (List et al., 2019) invites contributors to include their own data sets. This requires a transparent description of the contributing process.

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