The linguistic construal of disciplinarity: A data‐mining approach using register features

Abstract

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.

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

“…In terms of methods, the prevalent approach in studies of language variation and change in language use is frequency-based with a view to high-frequency features (e.g. Biber and Finegan 1989;Biber and Gray 2016;Degaetano-Ortlieb et al 2013;Fanego 1996;Moskowich and Crespo 2012;Rissanen et al 1997;Teich et al 2016). Other frequency bands, while potentially relevant, e.g.…”

Information Density and Linguistic Encoding (IDeaL)

“…Projects in Area B focus on surprisal in discourse context and different registers and text types. One of the projects, for example, looks at the hypothesis of linguistic densification in the evolution of scientific writing in English (mid 17th century to present), starting from the assumption that shared expertise of the author and their audience affects language use and, over a longer period, drives language change and the evolution of domainspecific language (register) [15]. As scientific activity in a given field develops and becomes more specialized, particular meanings become more predictable (within that scientific field).…”

Measuring ’Registerness’ in Human and Machine Translation: A Text Classification Approach

“…The higher the weight of a feature, the more distinctive it is for a class, regardless of its positive or negative sign. A feature ranking will help us to determine the relative discriminatory force of certain features specific for a particular register, as described by (Teich et al, 2015) in their work on register diversification in scientific writing.…”