Measuring language complexity from hierarchical reuse of recurring patterns
Researchers have developed a new metric called the ladderpath index to measure language complexity. This index quantifies the steps required to reconstruct a sequence by reusing recurring substructures, drawing from algorithmic information theory. When applied to 21 parallel corpora, the ladderpath index showed remarkable consistency across languages, suggesting a universal complexity level. The findings also indicate a trade-off between different linguistic levels, such as character inventory and vocabulary, supporting the idea that total complexity is conserved. AI
IMPACT Provides a novel, representation-independent method for analyzing linguistic complexity, potentially informing future NLP model development.