How to investigate implicit pragmatic phenomena in corpora
Bibliographic record
Abstract
Corpus pragmatics research mainly employs methods based on explicitly available, automatically searchable forms in corpora. However, there are pragmatic phenomena which do not have explicit forms; therefore, they are difficult to identify in corpora. The present paper aims to examine possibilities of studying implicit pragmatic phenomena in large corpora. Relying on the Hungarian Gigaword Corpus, it provides case studies on implicit arguments, conventional indirect speech acts and implicatures in Hungarian language use. The first case study analyses occurrences of the verb iszik ‘drink’ with implicit direct object arguments in its habitual reading ‘drink alcohol’, the second explores conventionally indirect directives with the verb tud ‘can’, and the third examines implicatures suggested in dispreferred second pair parts. The main conclusion of the paper is that only a corpus-based investigation is possible in studies of implicit pragmatic phenomena, but even this is restricted. Searching for certain explicit patterns in the corpus, combined with a manual, qualitative pragmatic analysis might lead us to identifying implicit pragmatic phenomena. Consequently, corpus methodology and traditional pragmatics research methods can be fruitfully combined. • Corpus-based method can be employed in the study of implicit pragmatic phenomena. • Traditional methods are indispensable in investigations of hidden pragmatic phenomena. • Implicit phenomena can be found in corpora by searching for certain explicit patterns. • Corpus study reveals a rich variety of linguistic devices to form indirect directives. • Sequential context plays a crucial role in a corpus-based study of implicatures.
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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 | 0.044 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".