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10 - Comparing Corpus-Driven and Corpus-Based Approaches to Diachronic Variation

Grammatical Changes in Late Modern and Present-Day English

from Part IV - Applications of Classification-Based Approaches

Published online by Cambridge University Press:  06 May 2022

By
Gerold Schneider
Edited by
Ole Schützler  and
Julia Schlüter
Ole Schützler
Affiliation:
Universität Leipzig
Julia Schlüter
Affiliation:
Universität Bamberg
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Summary

Focusing on grammatical changes in Late Modern and Present-Day English, the author applies a corpus-driven method to texts from two diachronic corpora, the Representative Corpus of Historical English Registers (ARCHER) and the Corpus of Historical American English (COHA). He compares his findings to those returned by more conventional corpus-based methods, which can be characterized as hypothesis-driven. To this purpose, the study employs automated profiling of large feature sets, such as word- and POS-based mono-, bi- and trigrams, chunks, syntactic dependency labels and measures of constituent order and length. The derived feature profiles are combined in a supervised classification task with a given division of texts into earlier and later corpus subperiods to reveal patterns of over- and underuse. Structures that profiled as over- or under-represented in the diachronic subsections are then browsed for grammatical changes that may have been missed by previous research. According to the author, an advantage of such approaches is that they are theory-neutral and may generate novel hypotheses for investigation. These may then serve as input to further corpus-based approaches.

Keywords

data-drivenmachine-learningsupervised classificationfeature weightPOS taggingparsing
Type
Chapter
Information
Data and Methods in Corpus Linguistics
Comparative Approaches
 , pp. 291 - 322
Publisher: Cambridge University Press
Print publication year: 2022

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References

Further Reading

Evert, Stefan. 2006. How Random Is a Corpus? The Library Metaphor. Zeitschrift für Anglistik und Amerikanistik 54(2). 177–90.CrossRefGoogle Scholar
Grimmer, Justin, and Stewart, Brandon. 2013. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis 21(3). 267–97.CrossRefGoogle Scholar
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Sinclair, John McHardy, and Carter, Ronald. 2004. Trust the Text: Language, Corpus and Discourse. London: Routledge.Google Scholar

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