Do non-native speakers create a pressure towards simplification? Corpus evidence

Berdičevskis, Aleksandrs

doi:10.12775/3991-1.007

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…In general, ranking languages of the world on a scale of complexity is not necessarily very productive or useful by itself. However, such measures are useful, and used for assessing effects of geographic, historical, social, cultural, political and cognitive variables on linguistic differences in specific domains of linguistic structure (e.g., McWhorter, 2001;Kusters, 2003;Bulté & Housen, 2012;Bentz et al, 2016;Bozic et al, 2007;Miestamo et al, 2008;Szmrecsanyi & Kortmann, 2012;Vainio et al, 2014;Mehravari et al, 2015;Yoon, 2017;Berdicevskis, 2018;Brezina & Pallotti, 2019;Chen & Meurers, 2019;De Clercq & Housen, 2019;Ehret & Szmrecsanyi, 2019;van der Slik et al, 2019;Weiss & Meurers, 2019;Michel et al, 2019). Since these studies use complexity metrics as a reference to linguistic differences based on other variables, it is crucial to have objective, precise and well-understood metrics.…”

Section: Introductionmentioning

confidence: 99%

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

Çöltekin¹,

Rama²

2022

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

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

Çöltekin¹,

Rama²

2022

Preprint

View full text Add to dashboard Cite

show abstract

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

Çöltekin

Rama

2022

Linguistics Vanguard

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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. Principal component analysis indicates that the first principal component explains 92.62 percent of the variation in eight measures, indicating a strong linear dependence between the complexity measures studied.

show abstract

Do non-native speakers create a pressure towards simplification? Corpus evidence

Cited by 2 publications

References 2 publications

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

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

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

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