A lectometric analysis of aggregated lexical variation in written Standard English with Semantic Vector Space models

Datos Bibliográficos

ID12621635
AutoresTom Ruette (KU Leuven, autor de correspondencia), Katharina Ehret (0000-0003-1117-3495, University of Freiburg), Benedikt Szmrecsanyi (0000-0001-8844-6602)
Año2016
Volumen21
Número1
Páginas48-79
Fecha de publicación2016-03-31
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaInternational Journal of Corpus Linguistics (JOURNAL)
Identificadores de la revistaISSN: 1384-6655 • E-ISSN: 1569-9811
EditorialJohn Benjamins Publishing Company (PUBLISHER • NL)
DOI10.1075/ijcl.21.1.03rue
OpenAlexW2321791494
IdiomaEN
Citas recibidas5
Referencias citadas33

Lectometry is a corpus-based methodology that explores how multiple language-external dimensions shape language usage in an aggregate perspective. The 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 find 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

Aggregate (composite · Dimension (graph theory · Lexical choice · Lexical density · Lexical functional grammar · Lexical item · Linguistics · Natural language processing · Perspective (graphical · Variation (astronomy · Variety (cybernetics · Authorship Attribution and Profiling · Computer Science · Linguistic Variation and Morphology · Mathematics · Natural Language Processing Techniques · Artificial Intelligence