Book contents
- Data and Methods in Corpus Linguistics
- Data and Methods in Corpus Linguistics
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Acknowledgements
- Introduction: Comparative Approaches to Data and Methods in Corpus Linguistics
- Part I Corpus Dimensions and the Viability of Methodological Approaches
- Part II Selection, Calibration and Preparation of Corpus Data
- 3 Comparing Approaches to (Sub-)Register Variation
- 4 Comparing Baselines for Corpus Analysis
- 5 Comparing Study Designs and Down-Sampling Strategies in Corpus Analysis
- Part III Perspectives on Multifactorial Methods
- Part IV Applications of Classification-Based Approaches
- Index
- References
5 - Comparing Study Designs and Down-Sampling Strategies in Corpus Analysis
The Importance of Speaker Metadata in the BNCs of 1994 and 2014
from Part II - Selection, Calibration and Preparation of Corpus Data
Published online by Cambridge University Press: 06 May 2022
Book contents
- Data and Methods in Corpus Linguistics
- Data and Methods in Corpus Linguistics
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Acknowledgements
- Introduction: Comparative Approaches to Data and Methods in Corpus Linguistics
- Part I Corpus Dimensions and the Viability of Methodological Approaches
- Part II Selection, Calibration and Preparation of Corpus Data
- 3 Comparing Approaches to (Sub-)Register Variation
- 4 Comparing Baselines for Corpus Analysis
- 5 Comparing Study Designs and Down-Sampling Strategies in Corpus Analysis
- Part III Perspectives on Multifactorial Methods
- Part IV Applications of Classification-Based Approaches
- Index
- References
Summary
This chapter throws into relief the importance of the link between corpus hits and their sources (i.e. texts or speakers) by comparing study designs uninformed and informed by such metadata. The authors’ argument draws attention to the consequences of uneven distributions of observations across the basic text units of a corpus. In the case study (a distributional analysis of the use of actually in the BNCs of 1994 and 2014), it is demonstrated that disproportionate word counts contributed by individual sources (in this case, speakers) will distort estimates for relevant subsections of corpus data (in this case, demographic groups defined by age and gender) if the analysis assigns the same weight to every observation. The proposed solution is to factor in the text (or speaker) level, but this hinges on the availability of the relevant metadata. Moreover, insights into the hierarchical structure of corpus data are shown to benefit the design stage of a study. Thus, if manual post-processing steps preclude an exhaustive analysis of corpus hits, insights into the organization of data points can direct down-sampling strategies to generate a statistically efficient subset of tokens.
Keywords
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- Chapter
- Information
- Data and Methods in Corpus LinguisticsComparative Approaches, pp. 127 - 160Publisher: Cambridge University PressPrint publication year: 2022
References
Further Reading
Ehrenberg, Andrew S. C. 1982. A Primer in Data Reduction. Chichester, UK: John Wiley & Sons. Chapter 7.Google Scholar
Johnson, Daniel E. 2014. Progress in Regression: Why Natural Language Data Calls for Mixed-Effects Models. Unpublished manuscript. www.danielezrajohnson.com/johnson_2014b.pdf.Google Scholar
Lohr, Sharon L. 2019. Sampling: Design and Analysis. Boca Raton, FL: CRC Press. Chapters 2, 3, 5 and 7.Google Scholar
Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: Sage. Chapter 8.Google Scholar
Winter, Bodo. 2020. Statistics for Linguistics. New York: Routledge. Chapters 14 and 15.Google Scholar
Winter, Bodo, and Grice, Martine. 2021. Independence and Generalizability in Linguistics. Linguistics 59(5). 1251–77.Google Scholar
References
Aijmer, Karin. 2002. English Discourse Particles: Evidence from a Corpus. Amsterdam: John Benjamins.Google Scholar
Aijmer, Karin. 2013. Understanding Pragmatic Markers: A Variational Pragmatic Approach. Edinburgh: Edinburgh University Press.Google Scholar
Aijmer, Karin. 2015. Analysing Discourse Markers in Spoken Corpora: Actually as a Case Study. In Baker, Paul and McEnery, Tony, eds. Corpora and Discourse Studies: Integrating Discourse and Corpora. New York: Palgrave Macmillan. 88–109.Google Scholar
Amrhein, Valentin, Trafimow, David and Greenland, Sander. 2019. Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication. The American Statistician, 73(sup1). 262–70. https://doi:10.1080/00031305.2018.1543137.Google Scholar
Andersen, Gisle. 2010. How to Use Corpus Linguistics in Sociolinguistics. In O’Keeffe, Anne and McCarthy, Michael, eds. The Routledge Handbook of Corpus Linguistics. New York: Routledge. 547–62.Google Scholar
Baker, Paul. 2010. Sociolinguistics and Corpus Linguistics. Edinburgh: Edinburgh University Press.Google Scholar
Brezina, Vaclav. 2018. Statistics in Corpus Linguistics: A Practical Guide. Cambridge: Cambridge University Press.Google Scholar
Brezina, Vaclav, Love, Robbie and Aijmer, Karin, eds. 2018. Corpus Approaches to Contemporary British Speech: Sociolinguistic Studies of the Spoken BNC2014. New York: Routledge.Google Scholar
Bürkner, Paul-Christian. 2017. brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software 80(1). 1–28.Google Scholar
Crowdy, Steve. 1995. The BNC Spoken Corpus. In Leech, Geoffrey, Myers, Greg and Thomas, Jenny, eds. Spoken English on Computer: Transcription, Mark-Up and Annotation. Harlow: Longman. 224–34.Google Scholar
Desagulier, Guillaume. 2017. Corpus Linguistics and Statistics with R: Introduction to Quantitative Methods in Linguistics. Cham: Springer.Google Scholar
Desgraupes, Bernard, and Loiseau, Sylvain. 2018. rcqp: Interface to the Corpus Query Protocol. R package version 0.5. https://CRAN.R-project.org/package=rcqp.Google Scholar
Ehrenberg, Andrew S. C. 1982. A Primer in Data Reduction. Chichester, UK: John Wiley & Sons.Google Scholar
Evert, Stefan. 2006. How Random Is a Corpus? The Library Metaphor. Zeitschrift für Anglistik und Amerikanistik 54(2). 177–90.Google Scholar
Evert, Stefan, and Hardie, Andrew. 2011. Twenty-First Century Corpus Workbench: Updating a Query Architecture for the New Millennium. In Proceedings of the Corpus Linguistics 2011 Conference. Birmingham, UK: University of Birmingham.Google Scholar
Gries, Stefan Th. 2017. Quantitative Corpus Linguistics with R: A Practical Introduction. New York: Routledge.Google Scholar
Hardie, Andrew. 2012. CQPweb: Combining Power, Flexibility and Usability in a Corpus Analysis Tool. International Journal of Corpus Linguistics 173. 380–409.Google Scholar
Krug, Manfred. 1998. Progressive and Conservative Modern British Dialects: Evidence from the BNC. Paper presented at the 19th ICAME Conference, Newcastle, Northern Ireland.Google Scholar
Krug, Manfred, and Lukas, Sönning. 2018. A Sociolinguistic Study of Actually in Spoken British English. Paper presented at ICAME39, Tampere. https://osf.Io/472dt.Google Scholar
Labov, William. 1972. Sociolinguistic Patterns. Philadelphia, PA: University of Philadelphia Press.Google Scholar
Labov, William. 1990. The Intersection of Sex and Social Class in the Course of Linguistic Change. Language Variation and Change 2. 205–54.Google Scholar
Lenk, Uta. 1998. Marking Discourse Coherence: Functions of Discourse Markers in Spoken English. Tübingen: Gunter Narr Verlag.Google Scholar
Lesnoff, Matthieu, and Lancelot, Renaud. 2019. aod: Analysis of Overdispersed Data. https://CRAN.R-project.org/package=aod. R package version 1.3.1.Google Scholar
Love, Robbie, Dembry, Claire, Hardie, Andrew, Brezina, Vaclav and Tony, McEnery. 2017. The Spoken BNC2014: Designing and Building a Spoken Corpus of Everyday Conversations. International Journal of Corpus Linguistics 22(3). 319–44.Google Scholar
Mair, Christian. 2006. Twentieth-Century English: History, Variation and Standardization. Studies in English Language. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Mosteller, Frederick, and Wallace, David L.. 1984. Applied Bayesian Inference: The Case of The Federalist Papers. New York: Springer.Google Scholar
Oh, Sun-Young. 2000. Actually and in Fact in American English: A Data-Based Analysis. English Language and Linguistics 4(2). 243–68.Google Scholar
Sarkar, Deepayan. 2008. Lattice: Multivariate Data Visualization with R. New York: Springer.Google Scholar
Sönning, Lukas, and Krug, Manfred. 2021. Actually in Contemporary British Speech: Data from the Spoken BNC Corpora. https://doi.org/10.18710/A3SATC. DataverseNO, V1, UNF:6:rp13HUEAY75735Bcul7eCg== [fileUNF].Google Scholar
Traugott, Elizabeth C., and Dasher, Richard B.. 2002. Regularity in Semantic Change. Cambridge: Cambridge University Press.Google Scholar
Vetter, Fabian. 2021. Issues of Corpus Comparability and Register Variation in the International Corpus of English: Theories and Computer Applications. Doctoral Dissertation, University of Bamberg. doi: https://doi.org/10.20378/irb-52406.Google Scholar
Waters, Cathleen M. 2011. Social and Linguistic Correlates of Adverb Variability in English. PhD dissertation. Department of Linguistics, University of Toronto.Google Scholar
Wickham, Hadley. 2016. ggplot2: Elegant Graphics for Data Analysis. New York: Springer.Google Scholar
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