We present the Royal Society Corpus (RSC) built from the Philosophical Transactions and Proceedings of the Royal Society of London. At present, the corpus contains articles from the first two centuries of the journal (1665―1869) and amounts to around 35 million tokens. The motivation for building the RSC is to investigate the diachronic linguistic development of scientific English. Specifically, we assume that due to specialization, linguistic encodings become more compact over time (Halliday, 1988; Halliday and Martin, 1993), thus creating a specific discourse type characterized by high information density that is functional for expert communication. When building corpora from uncharted material, typically not all relevant meta-data (e.g. author, time, genre) or linguistic data (e.g. sentence/word boundaries, words, parts of speech) is readily available. We present an approach to obtain good quality meta-data and base text data adopting the concept of Agile Software Development.

We present a methodology to analyze the linguistic evolution of scientific registers with data mining techniques, comparing the insights gained from shallow vs. linguistic features. The focus is on selected scientific disciplines at the boundaries to computer science (computational linguistics, bioinformatics, digital construction, microelectronics). The data basis is the English Scientific Text Corpus (SCITEX) which covers a time range of roughly thirty years (1970/80s to early 2000s) (Degaetano-Ortlieb et al., 2013; Teich and Fankhauser, 2010). In particular, we investigate the diversification of scientific registers over time. Our theoretical basis is Systemic Functional Linguistics (SFL) and its specific incarnation of register theory (Halliday and Hasan, 1985). In terms of methods, we combine corpus-based methods of feature extraction and data mining techniques.

In this paper, we present a methodology for the extraction of formulaic expressions, which goes beyond the mere extraction of candidate patterns. Using a pipeline we are able to extract information about the usage of formulaic expressions automatically from text corpora. According to Biber and Barbieri (2007) formulaic expressions are “important building blocks of discourse in spoken and written registers”. The automatic extraction procedure can help to investigate the usage and function of these recurrent patterns in different registers and domains. Formulaic expressions are commonplace not only in every- day language but also in scientific writing. Patterns such as 'in this paper', 'the number of', 'on the basis of' are often used by scientists to convey research interests, the theoretical basis of their studies, results of experiments, sci- entific findings as well as conclusions and are used as dis- course organizers. For Hyland (2008) they help to “shape meanings in specific context and contribute to our sense of coherence in a text”. We are interested in: (i) which and what type of formulaic expressions are used in scientific texts? (ii) the distribution of formulaic expression across different scien- tific disciplines, (iii) where do formulaic expressions occur within a text?