International Journal of Corpus Linguistics 21:1 (2016), 48–79. doi 10.1075/ijcl.21.1.03rue issn 1384–6655 / e-issn 1569–9811 © John Benjamins Publishing Company A lectometric analysis of aggregated lexical variation in written Standard English with Semantic Vector Space models Tom Ruette i , Katharina Ehret ii and Benedikt Szmrecsanyi i i KU Leuven / ii University of Freiburg Lectometry is a corpus-based methodology that explores how multiple language- external dimensions shape language usage in an aggregate perspective. Te paper combines this methodology with Semantic Vector Space modeling to investigate lexical variability in written Standard English, as sampled in the original Brown family of corpora (Brown, LOB, Frown and F-LOB). Based on a joint analysis of 303 lexical variables, which are semi-automatically extracted by means of a SVS, we fnd that lexical variation in the Brown family is systematically related to three lectal dimensions: discourse type (informative versus imaginative), standard variety (British English versus American English), and time period (1960s versus 1990s). It turns out that most lexical variables are sensitive to at least one of these three language-external dimensions, yet not every dimension has dedicated lexical variables: in particular, distinctive lexical variables for the real time dimension fail to emerge. Keywords: lectometry, lexis, aggregation, Semantic Vector Space models, Standard English 1. Introduction Tis paper presents a comprehensive analysis of lexical variation in written Standard English. Drawing on state-of-the-art lectometric methods (Geeraerts et al. 1999, Speelman et al. 2003), we explore the extent to which lexical choices in the Brown family of Standard English corpora (Hinrichs et al. 2010) are systemati- cally structured by three lectal dimensions, i.e. standard variety, discourse func- tion, and real time period. Our goal is to ofer a data-driven vision for the study of lexical variation by introducing Semantic Vector Space models as a means for