Toward an optimal code for communication: The case of scientific English

Abstract

We present a model of the linguistic development of scientific English from the mid-seventeenth to the late-nineteenth century, a period that witnessed significant political and social changes, including the evolution of modern science. There is a wealth of descriptive accounts of scientific English, both from a synchronic and a diachronic perspective, but only few attempts at a unified explanation of its evolution. The explanation we offer here is a communicative one: while external pressures (specialization, diversification) push for an increase in expressivity, communicative concerns pull toward convergence on particular options (conventionalization). What emerges over time is a code which is optimized for written, specialist communication, relying on specific linguistic means to modulate information content. As we show, this is achieved by the systematic interplay between lexis and grammar. The corpora we employ are the Royal Society Corpus (RSC) and for comparative purposes, the Corpus of Late Modern English (CLMET). We build various diachronic, computational n-gram language models of these corpora and then apply formal measures of information content (here: relative entropy and surprisal) to detect the linguistic features significantly contributing to diachronic change, estimate the (changing) level of information of features and capture the time course of change.

Registerial Adaptation vs. Innovation Across Situational Contexts: 18th Century Women in Transition

“…In our work on the linguistic development of English scientific writing, in addition to considering overall KLD tendencies, we inspect features contributing to periods of increased divergence (cf. Degaetano-Ortlieb and Teich, 2018 , 2019 ) enabling us to analyze reasons for and effects of change.…”

“…Nevalainen and Traugott, 2012 , p. 3). These assumptions are increasingly being challenged—most prominently by those exploring the probabilistic nature of language (Bod et al, 2003 ; Halliday, 2004 ), and also due to the application of statistical methods and data mining techniques to the analysis of temporal dynamics in language (Gries and Hilpert, 2010 ; Degaetano-Ortlieb and Teich, 2018 , 2019 ).…”

“…For example, Gries and Hilpert ( 2008 ) propose a specific clustering approach to analyze the development of English targeted at single linguistic phenomena, van Hulle and Kestemont ( 2016 ) use stylometric methods to periodize literary works of Beckett, and Popescu and Strapparava ( 2013 ) characterize epochs by a statistical approach. We have designed a data-driven periodization technique based on Kullback-Leibler divergence (henceforth KLD) that allows us to detect actual periods of change from the data itself, not confined to a particular linguistic phenomenon, but across linguistic levels (Degaetano-Ortlieb and Teich, 2018 , 2019 ). Formally, KLD measures how much two probability distributions (here: one for future and one for past language use) diverge from one another.…”

Linguistic Variation and Change in 250 Years of English Scientific Writing: A Data-Driven Approach

“…Apart from comparing predefined time periods with each other as is commonly done in diachronic corpus-linguistic studies (cf.Nevalainen and Traugott, 2012 for discussion), KLD can be used as a data-driven periodization technique (Degaetano-Ortlieb and Teich, 2018 , 2019 ). KLD is dynamically pushed over the time line comparing past and future (or, as KLD is asymmetric, future vs. past).…”

Divergence and the Complexity of Difference in Text and Culture

“…A recent study by Stefania Degaetano-Ortlieb and Elke Teich shows how scientific English became broader, or gradually came to enclose, general English over the second half of the nineteenth century. 23 There are certainly words that are common in general English that are less common in scientific English (for example, the word "wife"). However, there are (roughly speaking) far more words in scientific English that are rarely, if ever, used in general English (such as "oxide").…”