The rate of lexical replacement estimates the diachronic stability of word forms on the basis of how frequently a proto-language word is replaced or retained in its daughter languages. Lexical replacement rate has been shown to be highly related to word class and word frequency. In this paper, we argue that content words and function words behave differently with respect to lexical replacement rate, and we show that semantic factors predict the lexical replacement rate of content words. For the 167 content items in the Swadesh list, data was gathered on the features of lexical replacement rate, word class, frequency, age of acquisition, synonyms, arousal, imageability and average mutual information, either from published databases or gathered from corpora and lexica. A linear regression model shows that, in addition to frequency, synonyms, senses and imageability are significantly related to the lexical replacement rate of content words–in particular the number of synonyms that a word has. The model shows no differences in lexical replacement rate between word classes, and outperforms a model with word class and word frequency predictors only.

Computational methods have started playing a significant role in semantic analysis. One particularly accessible area for developing good computational methods for linguistic semantics is in color naming, where perceptual dissimilarity measures provide a geometric setting for the analyses. This setting has been studied first by Berlin & Kay in 1969, and then later on by a large data collection effort: the World Color Survey (WCS). From the WCS, a dataset on color naming by 2 616 speakers of 110 different languages is made available for further research. In the analysis of color naming from WCS, however, the choice of analysis method is an important factor of the analysis. We demonstrate concrete problems with the choice of metrics made in recent analyses of WCS data, and offer approaches for dealing with the problems we can identify. Picking a metric for the space of color naming distributions that ignores perceptual distances between colors assumes a decorrelated system, where strong spatial correlations in fact exist. We can demonstrate that the corresponding issues are significantly improved when using Earth Mover's Distance, or Quadratic -square Distance, and we can approximate these solutions with a kernel-based analysis method.

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The article discusses semantic change and lexical replacement processes in the color domain, based on color naming studies in seven Germanic languages (where diachronic intra-linguistic development is inferred from cross-linguistic synchronic studies) and from different generations of speakers in a single language (Swedish). Change in the color domain often begins and ends in conceptual peripheries, and I argue that this perspective is suitable for other semantic domains as well.