A Crosslingual Investigation of Conceptualization in 1335 Languages

TL;DR

This paper explores how different languages conceptualize the world by aligning concepts across 1,335 languages using a new method, revealing patterns of stability and similarity that complement traditional linguistic measures.

Contribution

It introduces Conceptualizer, a novel alignment method for crosslingual conceptualization analysis across many languages, with applications in understanding language similarity and stability.

Findings

Conceptualizer achieves good alignment accuracy for 'bird' and 32 Swadesh concepts.

Concreteness predicts crosslingual stability of concepts.

Conceptual similarity can classify languages into families with 54-87% accuracy.

Abstract

Languages differ in how they divide up the world into concepts and words; e.g., in contrast to English, Swahili has a single concept for `belly' and `womb'. We investigate these differences in conceptualization across 1,335 languages by aligning concepts in a parallel corpus. To this end, we propose Conceptualizer, a method that creates a bipartite directed alignment graph between source language concepts and sets of target language strings. In a detailed linguistic analysis across all languages for one concept (`bird') and an evaluation on gold standard data for 32 Swadesh concepts, we show that Conceptualizer has good alignment accuracy. We demonstrate the potential of research on conceptualization in NLP with two experiments. (1) We define crosslingual stability of a concept as the degree to which it has 1-1 correspondences across languages, and show that concreteness predicts stability. (2) We represent each language by its conceptualization pattern for 83 concepts, and define a similarity measure on these representations. The resulting measure for the conceptual similarity of two languages is complementary to standard genealogical, typological, and surface similarity measures. For four out of six language families, we can assign languages to their correct family based on conceptual similarity with accuracy between 54% and 87%.