Bibliographic record
Abstract
In [1], we study how causal relations may be used to improve narrative generation from real-life temporal data. We describe a method for extracting potential causal relations from temporal data and for structuring a generated report. The method is applied to the generation of reports highlighting unusual combinations of events in the Activity of Daily Living (ADL) domain. Our experiment applies association rules discovery techniques in [2] for selecting candidate associations based on three properties: frequency, confidence and significance. We assume that temporal proximity and temporal precedence are indicators of potential causality. The generation of a report from the ADL data for a given period follows a pipeline architecture. The first stage is data interpretation, which consists of finding instances of the previously selected association rules in the input. For each of those, one or more semantic relations are introduced as part of a hypothetic interpretation of the input data. Next those relations are used to plan the document as a whole in the document planning stage. The output is a rhetorical structure which is then pruned to keep only the most important events and relations. Follows a microplanning stage that plans the phrases and lexical units expressing the events and rhetorical relations. This produces a lexico-syntactic specification that is realised as natural language text in the last stage: surface realisation. After analysing the results, the extracted relations seem to be useful to locally link activities with explicit rhetorical relations. However, further work is needed to better exploit them for improving coherence at the global level.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".