Taking corpus variability into account in keyword analysis
(2008) 29th ICAME conference — Location: Ascona (Switzerland) (13.May.2008)
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- Abstract
- (en) Most studies that make use of keyword analysis rely on the log-likelihood or the chi-square to extract words that are particularly characteristic of a corpus (e.g. Scott & Tribble 2006). These measures are computed on the basis of absolute frequencies and cannot account for the fact that "corpora are inherently variable internally" (Gries 2007). To overcome this limitation, measures of dispersion are sometimes used in combination with keyness values (e.g. Rayson 2003; Oakes & Farrow 2007). Some scholars have also suggested using other statistical measures (e.g. t-test, Wilcoxon's rank-sum test) but these techniques have not gained corpus linguists' favour (yet?). One possible explanation for this lack of enthusiasm is that their statistical added value has rarely been discussed in terms of 'linguistic' added value. To the authors' knowledge, there is not a single study comparing keywords extracted by means of different measures. In our presentation, we will report on a follow-up study to Paquot (2007), which made use of the log-likelihood and measures of range and dispersion to extract academic words and design a productively-oriented academic word list. We make use of the log-likelihood, the t-test and the Wilcoxon's rank-sum test in turn to compare the academic and the fiction sub-corpora of the 'British National Corpus' and extract words that are typical of academic discourse. We compare the three lists of academic keywords on a number of criteria (e.g. number of keywords extracted by each measure, percentage of keywords that are shared in the three lists, frequency and distribution of academic keywords in the two corpora) and explore the specificities of the three statistical measures. We also assess the advantages and disadvantages of these measures for the design of an academic wordlist.
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Paquot, M., & Bestgen, Y. (2008). Taking corpus variability into account in keyword analysis. 29th ICAME conference, Ascona (Switzerland). https://hdl.handle.net/2078.5/226645