Bibliographic record
Abstract
Earley's parsing algorithm is an O(n^3) algorithm for parsing according to any context-free grammar. Its theoretical importance stems from the fact that it was one of the first algorithms to achieve this time bound, but it has also seen success in compiler-compilers, theorem provers and natural language processing. It has an elegant structure, and its time complexity on restricted classes of grammars is often as good as specialized algorithms. Grammars with ϵ-productions, however, require special consideration, and have historically lead to inefficient and inelegant implementations. In this thesis, we develop the algorithm from specification using the B-Method. Through refinement steps, we arrive at a list-processing formulation, in which the problems with ϵ-productions emerge and can be understood. The development highlights the essential properties of the algorithm, and has also lead to the discovery of an implementation optimization. We end by giving a concept-test of the algorithm as a literate Pascal program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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 teacher head, 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".