Complexity trade-offs and equi-complexity in natural languages: a meta-analysis

Area of Science:

• Linguistics
• Computational Linguistics
• Quantitative Linguistics

Background:

• Defining and measuring linguistic complexity remains a challenge with no universal consensus.
• Previous approaches often sought a single complexity value, overlooking diverse linguistic features.

Purpose of the Study:

• To propose a vector-based approach for quantifying language complexity using multiple measurements.
• To statistically evaluate the trade-off and equi-complexity hypotheses in linguistics.
• To analyze complexity across typologically diverse languages using meta-analysis.

Main Methods:

• Conducted meta-analyses of 28 distinct linguistic complexity metrics.
• Applied these metrics to texts from 80 typologically diverse languages.
• Utilized statistical correlation to assess relationships between complexity measures.

Main Results:

• The trade-off hypothesis received partial support, with approximately one-third of significant correlations being negative.
• The equi-complexity hypothesis was largely confirmed, indicating similar overall language complexity.
• Differences in complexity were observed in morphology and syntax, but overall complexity vectors were nearly indistinguishable.

Conclusions:

• A multi-dimensional vector approach offers a more nuanced view of language complexity.
• Despite variations in specific linguistic domains, languages exhibit remarkable overall complexity equivalence.
• Future research should explore the implications of equi-complexity for language acquisition and evolution.