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.

We analyze the linguistic evolution of selected scientific disciplines over a 30-year time span (1970s to 2000s). Our focus is on four highly specialized disciplines at the boundaries of computer science that emerged during that time: computational linguistics, bioinformatics, digital construction, and microelectronics. Our analysis is driven by the question whether these disciplines develop a distinctive language use-both individually and collectively-over the given time period. The data set is the English Scientific Text Corpus (SCITEX), which includes texts from the 1970s/1980s and early 2000s. Our theoretical basis is register theory. In terms of methods, we combine corpus-based methods of feature extraction (various aggregated features [part-of-speech based], n-grams, lexico-grammatical patterns) and automatic text classification. The results of our research are directly relevant to the study of linguistic variation and languages for specific purposes (LSP) and have implications for various natural language processing (NLP) tasks, for example, authorship attribution, text mining, or training NLP tools.

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We introduce IDEAL (Information Density and Linguistic Encoding), a collaborative research center that investigates the hypothesis that language use may be driven by the optimal use of the communication channel. From the point of view of linguistics, our approach promises to shed light on selected aspects of language variation that are hitherto not sufficiently explained. Applications of our research can be envisaged in various areas of natural language processing and AI, including machine translation, text generation, speech synthesis and multimodal interfaces.

We trace the evolution of Scientific English through the Late Modern period to modern time on the basis of a comprehensive corpus composed of the Transactions and Proceedings of the Royal Society of London, the first and longest-running English scientific journal established in 1665. Specifically, we explore the linguistic imprints of specialization and diversification in the science domain which accumulate in the formation of "scientific language" and field-specific sublanguages/registers (chemistry, biology etc.). We pursue an exploratory, data-driven approach using state-of-the-art computational language models and combine them with selected information-theoretic measures (entropy, relative entropy) for comparing models along relevant dimensions of variation (time, register). Focusing on selected linguistic variables (lexis, grammar), we show how we deploy computational language models for capturing linguistic variation and change and discuss benefits and limitations.