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 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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".