Semantic Property Grammars for Knowledge Extraction from Biomedical Text
Bibliographic record
Abstract
Abstract. We present Semantic Property Grammars, designed to extract concepts and relations from biomedical texts. The implementation adapts a CHRG parser we designed for Property Grammars [1], which views linguistic constraints as properties between sets of categories and solves them by constraint satisfaction, can handle incomplete or erroneous text, and extract phrases of interest selectively. We endow it with concept and relation extraction abilities as well. 1 Semantic Property Grammars (SPGs)- an introduction Property Grammars (PGs) [2] linguistically characterize sentences not in terms of an explicit, complete parse tree but in terms of seven simple properties between pairs of constituents, for instance, linearity (e.g., a determiner must precede a noun) or unicity (e.g., a noun can only have one determiner). A directly executable specification of PGs was developed by Dahl and Blache [3], which uses CHRG [4] to combine pairs of constituents according to whether properties between them are satisfied. SPGs are based on an adaptation of
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".