A computational approach to detecting the envelope of variation

Isaac L. Bleaman; Rhea Kommerell

doi:10.1515/lingvan-2023-0157

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A computational approach to detecting the envelope of variation

Isaac L. Bleaman und Rhea Kommerell

Veröffentlicht/Copyright: 25. September 2024

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Aus der Zeitschrift Linguistics Vanguard Band 10 Heft 1

Abstract

Variationist sociolinguistic methodology is grounded in the principle of accountability, which requires researchers to identify all of the contexts in which a given variable occurs or fails to occur. For morphosyntactic, lexical, and discourse variables, this process is notoriously time- and labor-intensive, as researchers manually sift through raw data in search of tokens to analyze. In this article, we demonstrate the usability of pretrained computational language models to automatically identify tokens of sociolinguistic variables in raw text. We focus on two English-language variables from different linguistic domains: intensifier choice (lexical; e.g., she is {very, really, so} smart) and complementizer selection (morphosyntactic; e.g., they thought {that, Ø} I understood). Text classifiers built with Bidirectional Encoder Representations from Transformers (BERT) achieve high precision and recall metrics for both variables, even with relatively little hand-annotated training data. Our findings suggest that computational language models can dramatically reduce the burden of preparing data for variationist analysis. Furthermore, by inspecting the classifiers’ scores for individual sentences, researchers can observe patterns that should be written into the description of the variable context for further study.

Keywords: variationist methodology; envelope of variation; large language models; intensifiers; complementizers

Corresponding author: Isaac L. Bleaman, University of California, Berkeley, USA, E-mail: bleaman@berkeley.edu

Acknowledgments

We gratefully acknowledge David Bamman, Lucy Li, Nicholas Tomlin, and audiences at Methods in Dialectology XVII, New Ways of Analyzing Variation 50, and the Sociolinguistics Lab at Berkeley for their feedback on this project. We also thank Sali Tagliamonte for sending us a dataset on intensifier variation (described in Tagliamonte and Roberts 2005), without which this research would not have been possible.

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Received: 2023-10-23

Accepted: 2024-06-10

Published Online: 2024-09-25

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https://doi.org/10.1515/lingvan-2023-0157

Schlagwörter für diesen Artikel

variationist methodology; envelope of variation; large language models; intensifiers; complementizers