# Colexifications for Bootstrapping Cross-lingual Datasets: The Case of Phonology, Concreteness, and Affectiveness

arXiv: https://arxiv.org/abs/2306.02646 · HTML: https://mlsanity.com/paper/2306.02646/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 73 references that are in the map.
- First posted on arXiv 5 Jun 2023.

## Builds on (most cited first, 18 of 18)

- [Learning Language Representations for Typology Prediction](https://mlsanity.com/paper/1707.09569/) (2017)
- [Phonotactic Complexity and its Trade-offs](https://mlsanity.com/paper/2005.03774/) (2020)
- [What do Language Representations Really Represent?](https://mlsanity.com/paper/1901.02646/) (2019)
- [On the Complexity and Typology of Inflectional Morphological Systems](https://mlsanity.com/paper/1807.02747/) (2018)
- [Conceptual similarity and communicative need shape colexification: an experimental study](https://mlsanity.com/paper/2103.11024/) (2021)
- [Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models](https://mlsanity.com/paper/2205.02023/) (2022)
- [From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings](https://mlsanity.com/paper/1802.09375/) (2018)
- [SIGTYP 2020 Shared Task: Prediction of Typological Features](https://mlsanity.com/paper/2010.08246/) (2020)
- [Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings](https://mlsanity.com/paper/2107.05038/) (2021)
- [Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs](https://mlsanity.com/paper/2305.12818/) (2023)
- [A Corpus for Large-Scale Phonetic Typology](https://mlsanity.com/paper/2005.13962/) (2020)
- [Does Typological Blinding Impede Cross-Lingual Sharing?](https://mlsanity.com/paper/2101.11888/) (2021)
- [Language Embeddings Sometimes Contain Typological Generalizations](https://mlsanity.com/paper/2301.08115/) (2023)
- [Probabilistic Typology: Deep Generative Models of Vowel Inventories](https://mlsanity.com/paper/1705.01684/) (2017)
- [A Probabilistic Generative Model of Linguistic Typology](https://mlsanity.com/paper/1903.10950/) (2019)
- [Tracking Typological Traits of Uralic Languages in Distributed Language Representations](https://mlsanity.com/paper/1711.05468/) (2017)
- [Uncovering Probabilistic Implications in Typological Knowledge Bases](https://mlsanity.com/paper/1906.07389/) (2019)
- [A Deep Generative Model of Vowel Formant Typology](https://mlsanity.com/paper/1807.02745/) (2018)

## Built on by (most cited first, 2 of 2)

- [Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis](https://mlsanity.com/paper/2410.13237/) (2024)
- [Patterns of Persistence and Diffusibility across the World's Languages](https://mlsanity.com/paper/2401.01698/) (2024)

## Abstract (arXiv)

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.