Keyness: Matching metrics to definitions

In this paper we examine the definitions of two widely-used interrelated constructs in corpus linguistics, keyness and keywords, as presented in the literature and corpus software manuals. In particular, we focus on a. the consistency of definitions given in different sources; b. the metrics used to calculate the level of keyness; c. the compatibility between definitions and metrics. Our survey of studies employing keyword analysis has indicated that the vast majority of studies examine a subset of keywords – almost always the top 100 keywords as ranked by the metric used. This renders the issue of the appropriate metric central to any study using keyword analysis. In this pilot study, we first argue that an appropriate, and therefore useful, metric for keyness needs to be fully consistent with the definition of keyword. We then use two sets of comparisons between corpora of different sizes, in order to test whether and to what extent the use of different metrics affects the ranking of keywords. More precisely, we look at the extent of overlap in the keyword rankings resulting from the adoption of different metrics, and we discuss the implications of ranking-based analysis adopting one metric or another. Finally, we propose a new metric for keyness, and demonstrate a simple way to calculate the metric, which supplements the keyword extraction in existing corpus software.