Corpus-based word frequencies are one of the most important predictors in language processing tasks. Frequencies based on conversational corpora are shown to better capture the variance in lexical decision tasks compared to traditional corpora. In this study, we show that frequencies computed from social media are currently the best frequency-based estimators of lexical decision reaction times. The results are robust and are still substantial when we control for corpus size.

A number of studies report that frequency is a poor predictor of acceptability, in particular at the lower end of the frequency spectrum. Because acceptability judgments provide a substantial part of the empirical foundation of dominant linguistic traditions, understanding how acceptability relates to frequency, one of the most robust predictors of human performance, is crucial. The relation between low frequency and acceptability is investigated using corpus‐ and behavioral data on the distribution of infinitival and finite that‐complements in Polish. Polish verbs (...) exhibit substantial subordination variation and for the majority of verbs taking an infinitival complement, the that‐complement occurs with low frequency (<0.66 ipm). These low‐frequency that‐clauses, in turn, exhibit large differences in how acceptable they are to native speakers. It is argued that acceptability judgments are based on configurations of internally structured exemplars, the acceptability of which cannot reliably be assessed until sufficient evidence about the core component has accumulated. (shrink)

This article correlates fine-grained semantic variability and change with measures of occurrence frequency to investigate whether a word’s degree of semantic change is sensitive to how often it is used. We show that this sensitivity can be detected within a short time span (i.e., 20 years), basing our analysis on a large corpus of German allowing for a high temporal resolution (i.e., per month). We measure semantic variability and change with the help of local semantic networks, combining elements of deep (...) learning methodology and graph theory. Our micro-scale analysis complements previous macro-scale studies from the field of natural language processing, corroborating the finding that high token frequency has a negative effect on the degree of semantic change in a lexical item. We relate this relationship to the role of exemplars for establishing form–function pairings between words and their habitual usage contexts. (shrink)