The subjective frequency of word n-grams
Bibliographic record
Abstract
When asked to think about the subjective frequency of an n-gram (a group of n words), what properties of the n-gram influence the respondent? It has been recently shown that n-grams that occurred more frequently in a large corpus of English were read faster than n-grams that occurred less frequently (Arnon & Snider, 2010), an effect that is analogous to the frequency effects in word reading and lexical decision. The subjective frequency of words has also been extensively studied and linked to performance on linguistic tasks. We investigated the capacity of people to gauge the absolute and relative frequencies of n-grams. Subjective frequency ratings collected for 352 n-grams showed a strong correlation with corpus frequency, in particular for n-grams with the highest subjective frequency. These n-grams were then paired up and used in a relative frequency decision task (e.g. Is green hills more frequent than weekend trips?). Accuracy on this task was reliably above chance, and the trial-level accuracy was best predicted by a model that included the corpus frequencies of the whole n-grams. A computational model of word recognition (Baayen, Milin, Djurdjevic, Hendrix, & Marelli, 2011) was then used to attempt to simulate subjective frequency ratings, with limited success. Our results suggest that human n-gram frequency intuitions arise from the probabilistic information contained in n-grams.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".