International Journal of Corpus Linguistics - Volume 25, Issue 2, 2020

Abstract

Throughout the social sciences, there has been growing pressure to present effect sizes when publishing empirical data (see American Psychological Association, 2001; Parsons & Nelson, 2004). While it seems indisputable that for the majority of quantitative research foci, effect size is an essential element of statistical analysis, this paper argues that specifically for key word analysis in corpus linguistics, the means of reporting effect size must depend on the level of the unit of study of each investigation (single text, collection or large corpus). After exploring some main criticisms of the log-likelihood measure, this paper unpacks the parameters of different measures for keyness and how they might address underlying concerns. It maintains that for the exploration of foregrounded/deviant/salient/marked features in text, the use of log-likelihood scores to rank the results is still fit for purpose and coupled with Bayes Factors is a solid approach for key word analyses.

Abstract

The present study looks at adverb placement in expert writing and in first-language and second-language novice spoken and written production. The extent to which first-language (L1) transfer is still present in advanced learners’ written production is also investigated. The study uses data from one expert corpus (LOCRA), two native-speaker student corpora (BAWE and LOCNEC) and two learner corpora (VESPA and LINDSEI). The results highlight the importance of taking mode into consideration, as clear distributional differences were found between spoken and written production. In addition, while considerable differences could be noted across L1 background in the spoken data, factors such as presence/absence of auxiliary, verb type (e.g. intransitive, copular/linking) and lexis were found to be most important for predicting adverb placement in the written data. Only very limited evidence of L1 transfer was found in the learners’ writing, suggesting that advanced learners have largely mastered the distributional preferences of adverbs.

Abstract

Turns-at-talk often do not start with their main business but rather with a pre-start (Sacks et al., 1974). This paper investigates the correlation of pre-starts with inserts, one of three major word classes (Biber et al., 1999). Based on the BNC’s mark-up, I investigate how inserts are positionally distributed in large amounts of turns of varied lengths. The analysis shows that inserts are overwhelmingly attracted to turn-first positions, the likely location of pre-starts. Further, in a subsample of 1,000 ten-word turns manually coded for pre-starts, 86% of all inserts serve a pre-start function. The findings call into question current speech processing models that fail to factor in turn structure. Further, pre-starts have crucial sequential and interactional implications as early indicators whether the new turn “agrees” with the prior turn and are likely key signals aiding listeners’ action ascription.

Abstract

This study explores the influence of corpus design when comparing lexical bundle use across groups, examining how the number of texts and average length of texts can impact conclusions about group differences. The study compares the use of lexical bundles by L1-English versus L2-English writers, based on analysis of two sub-corpora of academic articles that are matched for discipline, writer expertize, time of publication, and audience. However, the two sub-corpora differ with respect to the number of texts and the average length of texts. Three experiments examined the influence of differences in corpus composition. The results show that differences in the number of words and number of texts across sub-corpora can have a strong effect on claimed differences in bundle use across groups. This effect is found even when the texts in the corpora are closely matched for their register and topic.

This article reviews Corpus Linguistics for English Teachers: New Tools, Online Resources, and Classroom Activities

This article reviews Learner Corpus Research: New Perspectives and Applications