Productive Signs. A Computer-Assisted Analysis of Evolutionary, Typological, and Cognitive Dimensions of Word Families

Introduction

All human languages have simple and complex words. Simple words refer to meanings regardless of their form, while complex words are formed from other words, and their formation can be semantically motivated. Since words can share lexical material, we can group them into families.

Word Families

Word families can vary greatly in size, ranging from small ones comprising only a few members to large ones spanning several hundred words. However, it is still unclear why some words are more productive than others in forming new words.

Research Gaps

Lexical compositionality has received some attention in historical linguistics, linguistic typology, and cognitive linguistics. However, studies have mostly concentrated on the morphological complexity of individual words and languages. The fact that words form families which interact during language change and language use has typically been ignored.

As a result, many questions regarding word family formation remain unresolved, including:

1. How do word families evolve along language phylogenies?
2. Which semantic processes underlying word family formation are universal?
3. To what extent does human cognition influence the productivity of lexical roots to form families?

Project Objectives

The project will tackle these three target questions by unifying evolutionary, typological, and cognitive insights into lexical compositionality.

Methodology

Building on a computer-assisted framework that reconciles classical and computational approaches in historical linguistics and linguistic typology, the project will:

• Design new models to standardize cross-linguistic data on word families.
• Apply these models to integrate data from historical linguistics, linguistic typology, and cognitive linguistics.
• Develop new methods for the computer-assisted inference of word families, their underlying motivation patterns, and their evolutionary histories in large datasets.

Conclusion

In this way, the project will deepen the integration of cross-linguistic studies in cognitive and psychological sciences.