On the (non)utility of Juilland’s D to measure lexical dispersion in large corpora

Dados Bibliográficos

ID12621552
AutoresDouglas Biber (0000-0002-7024-505X, Northern Arizona University, autor correspondente), Randi Reppen (0000-0001-5657-9195, Northern Arizona University), Erin Schnur (Northern Arizona University), Romy Ghanem (Northern Arizona University)
Ano2016
Volume21
Fascículo4
Páginas439-464
Data de publicação2016-11-28
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoInternational Journal of Corpus Linguistics (JOURNAL)
Identificadores do periódicoISSN: 1384-6655 • E-ISSN: 1569-9811
EditoraJohn Benjamins Publishing Company (PUBLISHER • NL)
DOI10.1075/ijcl.21.4.01bib
OpenAlexW2559098455
IdiomaEN
Citações recebidas26
Referências citadas12

This paper explores the effectiveness of Juilland’s D as a measure of vocabulary dispersion in large corpora. Through a series of experiments using the BNC, we explored the influence of three variables: the number of corpus-parts used for the computation of D , the frequency of the target word, and the distributions of those words. The experiments demonstrate that the effective range for D is greatly reduced when computations are based on a large number of corpus-parts: even words with highly skewed distributions have D values indicating a relatively uniform distribution. We also briefly explore an alternative measure, Gries’ DP (Gries 2008), showing that it is a more reliable and effective measure of dispersion in a large corpus divided into many parts. In conclusion, we discuss the implications of these findings for quantitative methods applied to the creation of vocabulary lists as well as research questions in other areas of corpus linguistics

Algorithm · Computation · Corpus linguistics · Dispersion (optics · Linguistics · Measure (data warehouse · Natural language processing · Physics · Range (aeronautics · Vocabulary · Word (group theory · Computer Science · Engineering · Natural Language Processing Techniques · Philosophy · Second Language Acquisition and Learning · Topic Modeling · Artificial Intelligence