Stanford's Distantly-Supervised Slot-Filling System
Bibliographic record
Abstract
This paper describes the design and implementation of the slot filling system prepared by Stanford’s natural language processing group for the 2011 Knowledge Base Population (KBP) track at the Text Analysis Conference (TAC). Our system relies on a simple distant supervision approach using mainly resources furnished by the track’s organizers: we used slot examples from the provided knowledge base, which we mapped to documents from several corpora: those distributed by the organizers, Wikipedia, and web snippets. This system is a descendant of Stanford’s system from last year, with several improvements: an inference process that allows for multi-label predictions and uses worldknowledge to validate outputs; model combination; and a tighter integration of entity coreference and web snippets in the training process. Our submissions scored 16 F1 points using web snippets and 13.5 F1 without web snippets (both scores are higher than the median score of 12.7 F1). We also describe our temporal slot filling system, which achieved 37.0 F1 on the diagnostics temporal task on the developmental 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".