Extracting prototypes from exemplars What can corpus data tell us about concept representation?
Bibliographic record
Abstract
Abstract Over the past four decades, two distinct alternatives have emerged to rule-based models of how linguistic categories are stored and represented as cognitive structures, namely the prototype and exemplar theories. Although these models were initially thought to be mutually exclusive, shifts from one mechanism to the other have been observed in category learning experiments, bringing the models closer together. In this paper we implement a technique akin to varying abstraction modelling, that assumes intermediate abstraction processes to underlie category representations and categorization decisions; we do so using familiar statistical techniques such as regression and clustering that track frequency distributions in input. With this model we simulate, on the basis of actual usage of Russian try verbs and Finnish think verbs as observed in corpora, how prototypes for near-synonymous verbs could be formed from concrete exemplars at different levels of abstraction. In so doing, we take a closer look at the cognitive linguistic flirtation with multiple categorization theories, suggesting three improvements anchored in the fact that cognitive linguistics is a usage-based theory of language. Firstly, we show that language provides support for considering single prototype and full exemplar models as opposite ends along a continuum of abstraction. Secondly, we present a methodology that simulates how prototypes can be obtained from exemplars at more than one level of abstraction in a systematic and verifiable way. And thirdly, we illustrate our claims on the basis of work on verbs, denoting intangible events that are neither stable in nor independent of time and express relational concepts; this implies that verbs are more susceptible to their meanings being influenced by the concepts they relate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".