In this session, I will introduce the web application CLiC (Corpus Linguistics in Context), I will illustrate its use with a number of examples, and I will discuss wider implications for the study of literary texts. CLiC has been developed for the analysis of narrative fiction. It draws on methods from corpus linguistics and tailors them to the analysis of fiction. The CLiC corpora contain 154 texts and over 16 million words across five subcorpora: including Dickens’s novels, children’s fiction and the African American Writers corpus. For all CLiC texts, direct speech and specific places around speech have been marked up (Mahlberg et al. 2021). Hence, CLiC can run searches across defined textual subsets. In this way, it enables the analysis of specific features of narrative fiction. The CLiC Tagger is now available as a stand-alone tool, too, and I will show how researchers can use it to mark-up their own corpora. The study of fiction is an important area to bring corpus linguistics and digital humanities more closely together. In this session, I will also look at future opportunities in this space.
Suggested reading
Mahlberg, M., & Wiegand V. (2022). Exploring narrative fiction: corpora and digital humanities projects. In A. O’Keeffe & M. McCarthy (eds.), Routledge Handbook of Corpus Linguistics (2nd ed.). London: Routledge.
Egbert, J. & Mahlberg, M. (2020). Fiction – one register or two? Speech and narration in novels. Register Studies, 2(1), 72-101. https://doi.org/10.1075/rs.19006.egb
Mahlberg, M. and Wiegand, V. (2020). Stylistics and the digital humanities. In Conrad, S., Hartig, A., L. Santelmann (eds.) The Cambridge Introduction to Applied Linguistics, (pp. 219-234), Cambridge: Cambridge University Press.
Mahlberg, M., Stockwell, P., Wiegand, V. and Lentin, J. 2020. CLiC 2.1. Corpus Linguistics in Context. https://clic.bham.ac.uk/
Mastropierro, L. & Mahlberg, M., (2017). Key words and translated cohesion - A corpus stylistic analysis of Lovecraft’s At the Mountains of Madness and its Italian translation, English Text Construction. 10 (1), 78-105. https://doi.org/10.1075/etc.10.1.05mas