Bibliographic record
Abstract
The representation of temporal information in text represents a significant computational challenge.The problem is particularly acute in the case of literary texts, which tend to be massively underspecified, often relying on a network of semantic relations to establish times and timings.This paper shows how a model based on threaded directed acyclic graphs makes it possible to capture a range of subtle temporal information in this type of text and argues for an onomasiological approach which places meaning before form. Time and TextThis paper deals with the representation of temporal phenomena in literary texts.As a result, it builds on work from a number of fields. 1 Thus, it takes as given the longstanding observation from philosophy that time is not a simple issue of days, months and years, but reflects issues of perception and culture (Ricoeur, 1983).At the same time, it assumes that the study of temporal phenomena will be enhanced by use of a formal representation (Allen, 1984).It further assumes the traditional narratological distinction between the information which underlies a text, variously known as the fabula or histoire and which we will henceforth call the story and some particular instantiation of this in text, often called the sjuzhet or récit, which we will henceforth call the narrative (Genette, 1972).Mani (2010), based on earlier work, suggests that the temporal relations BEFORE, DURING, IMMEDIATELY BEFORE, BEGINS, ENDS, SI-MULTANEOUS AND OVERLAPS are adequate for representing time in human languages.This raises the interesting empirical question of how 1 In what follows, for lack of space, we restrict ourselves to citing some typical examples from a vast literature.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".