# What do complexity measures measure? Correlating and validating corpus-based measures of morphological complexity

arXiv: https://arxiv.org/abs/2204.05056 · HTML: https://mlsanity.com/paper/2204.05056/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 61 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection](https://mlsanity.com/paper/1910.11493/) (2019)
- [The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection](https://mlsanity.com/paper/1810.07125/) (2018)
- [On the Complexity and Typology of Inflectional Morphological Systems](https://mlsanity.com/paper/1807.02747/) (2018)
- [The statistical trade-off between word order and word structure - large-scale evidence for the principle of least effort](https://mlsanity.com/paper/1608.03587/) (2016)

## Built on by (most cited first, 8 of 8)

- [Confounding Factors in Relating Model Performance to Morphology](https://mlsanity.com/paper/2511.01380/) (2025)
- [DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages](https://mlsanity.com/paper/2205.12215/) (2022)
- [A Morphology-Based Investigation of Positional Encodings](https://mlsanity.com/paper/2404.04530/) (2024)
- [Killkan: The Automatic Speech Recognition Dataset for Kichwa with Morphosyntactic Information](https://mlsanity.com/paper/2404.15501/) (2024)
- [Annotating and Inferring Compositional Structures in Numeral Systems Across Languages](https://mlsanity.com/paper/2503.01625/) (2025)
- [The Model's Language Matters: A Comparative Privacy Analysis of LLMs](https://mlsanity.com/paper/2510.08813/) (2025)
- [Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Models](https://mlsanity.com/paper/2602.03551/) (2026)
- [Measuring language complexity from hierarchical reuse of recurring patterns](https://mlsanity.com/paper/2606.11531/) (2026)

## Abstract (arXiv)

We present an analysis of eight measures used for quantifying morphological complexity of natural languages. The measures we study are corpus-based measures of morphological complexity with varying requirements for corpus annotation. We present similarities and differences between these measures visually and through correlation analyses, as well as their relation to the relevant typological variables. Our analysis focuses on whether these `measures' are measures of the same underlying variable, or whether they measure more than one dimension of morphological complexity. The principal component analysis indicates that the first principal component explains 92.62 % of the variation in eight measures, indicating a strong linear dependence between the complexity measures studied.