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Annotating and Inferring Compositional Structures in Numeral Systems Across Languages

Published: March 3, 2025 | arXiv ID: 2503.01625v2

By: Arne Rubehn , Christoph Rzymski , Luca Ciucci and more

Potential Business Impact:

Helps computers understand number words in any language.

Business Areas:
Natural Language Processing Artificial Intelligence, Data and Analytics, Software

Numeral systems across the world's languages vary in fascinating ways, both regarding their synchronic structure and the diachronic processes that determined how they evolved in their current shape. For a proper comparison of numeral systems across different languages, however, it is important to code them in a standardized form that allows for the comparison of basic properties. Here, we present a simple but effective coding scheme for numeral annotation, along with a workflow that helps to code numeral systems in a computer-assisted manner, providing sample data for numerals from 1 to 40 in 25 typologically diverse languages. We perform a thorough analysis of the sample, focusing on the systematic comparison between the underlying and the surface morphological structure. We further experiment with automated models for morpheme segmentation, where we find allomorphy as the major reason for segmentation errors. Finally, we show that subword tokenization algorithms are not viable for discovering morphemes in low-resource scenarios.

Page Count
13 pages

Category
Computer Science:
Computation and Language