JVN-TDT Entity Linking Systems at TAC-KBP2012
Bibliographic record
Abstract
We present two methods for entity linking in two of our systems submitted to TAC/KBP 2012. The first one, namely Method 1, learns coherence among co/occurrence entities re/ ferred to within a text by exploiting Wikipe/ dia’s link structure and the second one, namely Method 2, combines some heuristics with a statistical model for entity linking. Method 1 exploits two features to train a classifier and exploits coreference relations among co/ occurring mentions. Method 2 is a hybrid me/ thod containing two phases. The first phase is a rule/based phase that filters candidates and, if possible, it disambiguates mentions with high reliability. The second phase employs a statis/ tical model to rank the candidates of each re/ maining mention and choose the one with the highest ranking as the right referent of that mention. Experiments are conducted to eva/ luate two methods on two datasets – TAC/ KBP2011 and TAC/KBP2012 datasets.
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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".