Bibliographic record
Abstract
This paper describes the Knowledge Resolver system (KRes) and its performance on the TAC-KBP 2013 English Slot Filling task. KRes is a logic-based inference system aimed at improving statistical relation extraction by deduction and abduction inference towards the best document-level interpretation. For the 2013 evaluation we developed an initial KRes system that extracts a subset of seven TACKBP relations using manually constructed dependency patterns in concert with entity type and name-linking rules. For our baseline extraction engine we used the Blender Lab’s KBP-Toolkit 1.5, which was also exploited at the front-end of KRes for its document indexing, selection and name expansion capabilities. Instead of trying to improve upon KBPToolkit results using inference, for this year we simply combined its results with those of KRes for our best system which landed us in the middle of the pack (only addressing 13 out of the 40 KBP slot types). We also report results for KRes relativized to the seven slot types it addressed which shows promise for future evaluations.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.029 |
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".