Adapting a General Semantic Interpretation Approach to Biological Event Extraction
Bibliographic record
Abstract
The second BioNLP Shared Task on Event Extraction (BioNLP-ST’11) follows up the previous shared task competition with a focus on generalization with respect to text types, event types and subject domains. In this spirit, we re-engineered and extended our event extraction system, emphasizing linguistic generalizations and avoiding domain-, event typeor text type-specific optimizations. Similar to our earlier system, syntactic dependencies form the basis of our approach. However, diverging from that system’s more pragmatic nature, we more clearly distinguish the shared task concerns from a general semantic composition scheme, that is based on the notion of embedding. We apply our methodology to core bio-event extraction and speculation/negation detection tasks in three main tracks. Our results demonstrate that such a general approach is viable and pinpoint some of its shortcomings. 1
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".