Register versus idiolect variation

Václav Cvrček

Zuzana Komrsková

David Lukeš

Petra Poukarová

Anna Řehořková

Adrian Jan Zasina

24th September 2018

Introduction

MDA framework

  • language variability can be caused by several different phenomena, e.g.
    • register
    • author/speaker
  • MD model based on the Koditex corpus
    • 8 dimensions
    • explains 56% of variability
    • position of a text in 8D space

Research questions

How can the MD model help:

  • in analyzing elicited data?
  • in quantifying differences between elicitation scenarios?
  • in analyzing register versus idiolect variation?

CPACT data

Speakers

  • data collected within CPACT project (GA ČR 16-19087S, D. Kučera)

  • 200 native speakers of Czech
    • proportionate stratified sampling
    • selected according to the criteria of age (15–24, 25–34, 35–55 and 55+), gender, highest achieved level of education
    • rich psychological metadata – results from several psychological tests, e.g. Big Five personality traits (extraversion, conscientiousness, openness to experience, agreeableness, neuroticism), DASS 21 (Depression, Anxiety, Stress Scale) etc.

Texts

  • each participant wrote 4 texts within one day
  • requirements:
    • length: 180–200 words
    • form: letter
    • follow task scenario
  • scenarios describe situational setting + motivation

  • defined by 2 criteria, formality and interpersonal stance:

    Informal Formal
    Dominant Letter from vacation Letter of complaint
    Submissive Letter of apology Cover letter
  • analysis – same set of features as used in original MDA

  • results projected onto original MD model

Register variability of elicited texts

Validity of elicited texts

  • psychologically oriented analyses of linguistic behavior often based on texts elicited through carefully devised scenarios (e.g. Knapp, et al., 1974; Fisher, 1993; Newman, et al., 2003)
    • advantages: controlled environment, diversity of scenarios \(\Rightarrow\) diversity of elicited data


  • Are there systematic differences between texts elicited by different scenarios?
  • How to design scenarios to be meaningfully distinct in terms of the language they elicit?
  • Which scenarios contribute to the diversity of the resulting material?

Elicited texts in 4 dimensions

Differences between scenarios

  • position of elicited texts (CPACT) corresponds to similar non-elicited texts (Koditex)
  • each task = specific situation, setting
    • formal tasks (cover letter, letter of complaint): large overlap in the MD model
    • letter from vacation: greatest within-task variability
  • scenarios differ in “degrees of freedom” given to author
    • the more rigid and highly conventionalized a type of text is, the less room for individual variability

Register versus idiolect

Motivation and overview

CPACT data:

  • a unique opportunity to study the interference of idiolect
  • approximations:
    • idiolect = 200 speakers – balanced stratified sampling
    • register = 4 scenarios (tasks) – situation (formality), context (stance)
    • all texts are letters – the difference between scenarios should be subtle (!)

Methods

  • Statistical modeling:
    • ANOVA – effect size (\(\eta\))
    • Kruskal-Wallis test – effect size (\(E_{R}^2\))
    • Linear Mixed-effects models (LMER) – coefficient of determination (\(R^2\))
  • Intuitive approach:
    • determining average distance in MD space

Results – statistical models

Surprisingly (?) similar results:

Results – statistical models

Proportion of variation accounted for by task and author in all dimensions (weighted with regards to the “importance” of individual dimensions):

Method wAverage.Task wAverage.Author
ANOVA 0.6119 0.3881
Kruskal-Wallis 0.6279 0.3721
LMER 0.7273 0.2727

Intuitive approach: distances between texts

Same author (black) vs. identical register (red)

diagram

Results – distances

Central tendency and spread of distances:

group mean median sd mad
same_auth 1.460 1.418 0.539 0.546
same_task 1.096 1.048 0.375 0.359

This is in agreement with the statistical models:

  • texts of one author differ more than texts based on one scenario
  • → scenario (task) is a better predictor of position within MD space

Conclusions

Conclusions

  • an MD model can help in assessing the ecological validity of elicited texts
    • are they what/where we expected them to be?
  • projecting elicited data onto the MD model can reveal key differences (or overlaps) between scenarios
  • controlled settings of CPACT data allow for the combined study of idiolect and register variation

References

  • Bermel, N. (2014): Czech Diglossia: Dismantling or Dissolution? In J. Árokay et al. (eds), Divided Languages? Springer.
  • Biber, D. (1991): Variation across speech and writing. Cambridge: Cambridge University Press
  • Biber, B. & Conrad, S. (2009): Register, Genre, and Style. New York, NY: Cambridge University Press.
  • Cvrček, V. & Chlumská, L. (2015): Simplification in translated Czech: a new approach to type-token ratio. Russian linguistics 39/3, (p. 309–325).
  • Fisher, R. J. (1993). Social Desirability Bias and the Validity of Indirect Questioning. Journal of Consumer Research 20(2), (p. 303–315).
  • Knapp, M. L., Hart, R. P., & Dennis, H. S. (1974). An Exploration of Deception as a Communication Construct. Human Communication Research 1(1), (p. 15–29).
  • Newman, M. L., Pennebaker, J. W., Berry, D. S., & Richards, J. M. (2003). Lying words: predicting deception from linguistic styles. Personality & Social Psychology Bulletin 29(5), (p. 665–675).
  • Popescu, I., Best, K. & Altmann, G. (2007): On the dynamics of word classes in texts. Glottometrics 14, (p. 58–71).

Acknowledgments

This research was supported by:

  • the ERDF project Language Variation in the CNC no. CZ.02.1.01/0.0/0.0/16_013/0001758,
  • and the CPACT research project funded by the Czech Science Foundation, grant nr. GA ČR 16-19087S.

It builds upon work made possible by the Czech National Corpus project (LM2015044) funded by MEYS within the framework of Large RDI Infrastructures.

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