Quantifying the evolutionary dynamics of language

By Erez Lieberman, Jean-Baptiste Michel, J. Jackson, Tina Tang, Martin A. Nowak. 2007

448 citations recorded by the source corpus.

Human language is based on grammatical rules. Cultural evolution allows these rules to change over time. Rules compete with each other: as new rules rise to prominence, old ones die away. To quantify the dynamics of language evolution, we studied the regularization of English verbs over the past 1,200 years. Although an elaborate system of productive conjugations existed in English’s proto-Germanic ancestor, Modern English uses the dental suffix, ‘-ed’, to signify past tense. Here we describe the emergence of this linguistic rule amidst the evolutionary decay of its exceptions, known to us as irregular verbs. We have generated a data set of verbs whose conjugations have been evolving for more than a millennium, tracking inflectional changes to 177 Old-English irregular verbs. Of these irregular verbs, 145 remained irregular in Middle English and 98 are still irregular today. We study how the rate of regularization depends on the frequency of word usage. The half-life of an irregular verb scales as the square root of its usage frequency: a verb that is 100 times less frequent regularizes 10 times as fast. Our study provides a quantitative analysis of the regularization process by which ancestral forms gradually yield to an emerging linguistic rule.

Subfields: Cultural Studies, Language and Linguistics, Natural Language Processing

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Citations in the corpus

Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change (2016, methodology): “We trained models on the 6 datasets described in Table 1, taken from Google N-Grams (Lin et al., 2012) and the COHA corpus (Davies, 2010).”
A Computational Evaluation of Two Laws of Semantic Change (2015, background): “…laws that characterize the nature of these changes, and the literature contains laws of sound change (Labov, 2010), grammatical or syntactic change (Hopper & Traugott, 2003; Lieberman, Michel, Jackson, Tang, & Nowak, 2007), and semantic change (Bréal, 1897; Sweetser, 1991; Traugott & Dasher, 2002).”
Cultural Evolution: A Review of Theory, Findings and Controversies (2015, methodology): “Here, Darwin went further than merely applying the notion of common descent to languages, as the linguists had done.”
Towards the Quantification of the Semantic Information Encoded in Written Language (2009, background): “Language has developed following the laws of natural evolution [14, 18, 19] with the functional goal of encoding and transmitting information between humans.”
Words as alleles: connecting language evolution with Bayesian learners to models of genetic drift (2010, background): “For example, words such as one , who or night are at the slow end of the distribution, while words such as dirty or guts are replaced at a much faster rate (Lieberman et al. 2007).”
Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media (2020, background): “Clyne et al. in [26] discuss the changing of words meaning by the inﬂuence of immigrant languages, Lieberman et al. [27], instead, try to quantify these changes in the common language.”
Quantitative Analysis of Culture Using Millions of Digitized Books (2010, background): “We studied the English irregular verbs, a classic model of grammatical change (14-17).”
Temporal Analysis of Language through Neural Language Models (2014, background): “Existing words adopt additional senses ( gay ), new words are created ( internet ), and some words ‘die out’ (many irregular verbs, such as burnt , are being replaced by their regularized counterparts (Lieberman et al., 2007)).”
Syntactic Annotations for the Google Books NGram Corpus (2012, background): “For instance, most English verbs are regular (their past tense is formed by adding -ed), and the few exceptions, known as irregular verbs, tend to regularize over the centuries (Lieberman et al., 2007).”