A prolog implementation of a subset of Marcus’ parser and its relation to the handling of extragrammatical input
Bibliographic record
Abstract
In any system employing a natural language interface, there is the problem that, by means of a formal grammar, the system itself defines the language it will accept. But, when using language, people will not always adhere to the rules of this grammar; therefore, a natural language computer system should not simply treat as incomprehensible any input not conforming to its internal grammar, input we may call extragrammatical. The term extragrammatical refers to input that is not necessarily incorrect in an absolute sense but only relative to the formal scope of a system's grammar. Before a truly robust system can be developed, what is needed is a parsing mechanism that enforces grammaticality where possible, and this implies a deterministic approach to natural language parsing. This thesis discusses the importance of flexible natural language interfaces; the notion of extragrammatical language and its connexion to robust parsing; a deterministic parser, PARSIFAL, developed by Mitchell Marcus; and a reimplementation, using logic programming, of a subset of Marcus' system. Programming was done with CProlog on a VAX 11/750* running 4.2 BSD UNIX.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".