Linguistic Resources for 2012 Knowledge Base Population Evaluations
Bibliographic record
Abstract
Knowledge Base Population (KBP) is an evaluation track of the Text Analysis Conference (TAC), a workshop series organized by the National Institute of Standards and Technology (NIST). In 2013, the KBP evaluations included five tasks targeting information extraction and question answering technologies: Entity Linking, Slot Filling, Temporal Slot Filling, Sentiment Slot Filling, and Cold Start. The Sentiment and Temporal Slot Filling tasks were introduced in 2013 in an effort to move the KBP challenges into new domains, specifically beliefs and events. Linguistic Data Consortium (LDC) has supported the TAC KBP evaluation since 2009, each year producing new linguistic resources including data, annotations, system assessments, tools and specifications. This paper describes the resource creation efforts in support of TAC KBP 2013, with an emphasis on procedures and methodologies for query selection, annotation, and assessment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".