Formal and informal meaning from documents through skeleton sentences
Bibliographic record
Abstract
In [ Sperberg-McQueen et al. 2000a ], Sperberg-McQueen et al. describe a framework in which the semantics of a structured document is represented by the set of inferences (statements) licensed by the document, that is, statements which can be considered to hold on the basis of the document. The authors suggest that an adequate set of basic inferences can be generated from the document itself by a fairly simple skeleton sentence and deictic expression mechanism. These ideas were taken up and developed in various ways and contexts in later work (see for example [ Sperberg-McQueen et al. 2002 ]) and came to be called the “Formal tag-set description” approach (FTSD). The approach is independent of any particular logical system, and the possibility that the statements licensed by a document be in natural language has been mentioned and exemplified, though not to a large extent. With a different set of preoccupations in mind (namely, providing semantic support to an author during the document creation process), Marcoux introduced in [ Marcoux 2006 ] intertextual semantics (IS), a framework in which the meaning of a document is entirely and exclusively represented by natural language segments. In this paper, we compare the IS and FTSD approaches, and argue that the insights into the meaning of a document supplied by the two approaches actually complement each other. We give a number of concrete examples of increasing complexity, including the set of formal and informal statements derivable in each case, to substantiate our claim.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".