HITS' Monolingual and Cross-lingual Entity Linking System at TAC 2012: A Joint Approach.
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents HITS’ system for monolingual and cross-lingual entity linking at TAC 2012. We propose a joint system for entity disambiguation, recognition of NILs and clustering using Markov Logic. The proposed model (1) is global, i.e. a group of mentions in a text is disambiguated in one single step combining various global and local features, and (2) performs disambiguation, unknown entity detection and clustering jointly. The model for all languages is exclusively trained on English Wikipedia articles. The results achieved in the TAC monolingual and cross-lingual entity linking tasks show that our approach is competitive: our best English run achieves 8.5 percent points above median, while we outperformed all other participating systems in the Chinese cross-lingual subtask. The results for the Spanish subtask are lower due to a bug. Our unofficial Spanish results (after fixing the bug) are close to the ones of the best system.
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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 it