  Colexification refers to the linguistic phenomenon where a single lexical
form is used to convey multiple meanings. By studying cross-lingual
colexifications, researchers have gained valuable insights into fields such as
psycholinguistics and cognitive sciences [Jackson et al.,2019]. While several
multilingual colexification datasets exist, there is untapped potential in
using this information to bootstrap datasets across such semantic features. In
this paper, we aim to demonstrate how colexifications can be leveraged to
create such cross-lingual datasets. We showcase curation procedures which
result in a dataset covering 142 languages across 21 language families across
the world. The dataset includes ratings of concreteness and affectiveness,
mapped with phonemes and phonological features. We further analyze the dataset
along different dimensions to demonstrate potential of the proposed procedures
in facilitating further interdisciplinary research in psychology, cognitive
science, and multilingual natural language processing (NLP). Based on initial
investigations, we observe that i) colexifications that are closer in
concreteness/affectiveness are more likely to colexify; ii) certain
initial/last phonemes are significantly correlated with
concreteness/affectiveness intra language families, such as /k/ as the initial
phoneme in both Turkic and Tai-Kadai correlated with concreteness, and /p/ in
Dravidian and Sino-Tibetan correlated with Valence; iii) the type-to-token
ratio (TTR) of phonemes are positively correlated with concreteness across
several language families, while the length of phoneme segments are negatively
correlated with concreteness; iv) certain phonological features are negatively
correlated with concreteness across languages. The dataset is made public
online for further research.
