Generalizing from Freebase and Patterns using Cluster-Based Distant Supervision for TAC KBP Slotfilling 2012.
Bibliographic record
Abstract
For the slot filling task of TAC KBP 2012 we extended last year’s system in several respects. The core of the system is a set of semisupervised per-relation classifiers, trained by a scheme known as distant supervision. Training data are generated by using Freebase and applying patterns. Relation models rely on (1) word clusters generalizing from context surface forms and (2) additional argument-level features. For the retrieval of answer candidates, we use document retrieval in combination with an entity expansion model based on Wikipedia link texts. We do not use a separate sentence retrieval step and rely entirely on the classifier for filtering out bad candidates. Our system does not rely on any syntactic analysis or co-reference resolution. The best-ranked run of the full system achieves an F-score of 23.4% on the official test queries.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".