Bibliographic record
Abstract
We describe a project undertaken by an interdisciplinary team of researchers in sleep and in and machine learning. The goal is sentiment extraction from a corpus containing short textual descriptions of dreams. Dreams are categorized in a four-level scale of affections. The approach is based on a novel representation, taking into account the leading themes of the dream and the sequential unfolding of associated affective feelings during the dream. The dream representation is based on three combined parts, two of which are automatically produced from the description of the dream. The first part consists of co-occurrence vectors, which — unlike the standard Bag-of-words model — capture non-local relationships between meanings of word in a corpus. The second part introduces the dynamic representation that captures the change in affections throughout the progress of the dream. The third part is the self-reported assessment of the dream by the dreamer according to eight given attributes. The three representations are subject to aggressive feature selection. Using an ensemble of classifiers and the combined 3-partite representation, we have achieved 64% accuracy, which is in the range of human experts' consensus in that domain.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".