An information-theoretic view on language complexity and register variation: Compressing naturalistic corpus data
Bibliographic record
Abstract
Abstract This article utilises an innovative, information-theoretic metric to assess complexity variation across written and spoken registers of British English. This is novel because previous research on language complexity mainly analysed complexity variation in typological data, single language case studies or geographical varieties of the same language. The measure boils down to Kolmogorov complexity which can be conveniently approximated with off-the-shelf compression programs. Essentially, text samples that can be compressed more efficiently count as linguistically simple. The dataset covers a wide range of traditional written and spoken registers (e.g. broadsheet newspapers, courtroom debate or face-to-face conversation), as sampled in theBritish National Corpus. It turns out that Kolmogorov-based register variation coincides with register formality such that informal registers are overall and morphologically less complex than more formal registers, but more complex in regard to syntax (defined here as rigid word order). Generally, the results show that written and spoken registers vary along a continuum, and significantly trade-off morphological against syntactic complexity (and vice versa). Finally, the findings support proposals to view language as a complex adaptive system and demonstrate how language adapts to the situational context of language production and functional-communicative needs of its users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".