Semantic Distinctions in Cognitive Verb-Preposition Combinations: A Corpus-Based Analysis of Of and About
Bibliographic record
Abstract
The current paper explores the semantic distinctions of cognitive verbs followed by the prepositions of and about through corpus methods, framed within Construal theory, backgrounded in Cognitive Grammar (Langacker, 1986). Construal theory suggests that meaning is shaped by how speakers conceptualize the world around them. The study examines how these verb-preposition combinations reflect different conceptualizations, where verb+ of encodes a more limited partitive construal meaning, while verb+ about signals broader and more holistic construal meanings. The analysis demonstrates that of is used for selective and abstract meanings (recalling and imagining ideas), whereas about implies a closer and more concrete involvement in a given situation. Through using frequency, dispersion, distributional, and collocation measures, the findings demonstrate that of and about systematically alter verb semantics, confirming the construal framework. Differences in frequency of use appear clearly in COCA and BNC, which might be due to regional preferences. Dispersion analysis shows think of/about are more common in spoken English. Know about is more frequent than know of, especially in spoken discourse. Genre analysis reveals different usage patterns in fiction, TV, and blog genres, expressing imaginative situations, feelings, and ideas. The study underscores the interplay between prepositional semantics and usage in context, offering insights for lexicography and theoretical semantics of verb-preposition interaction.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".