Wikipedia Search as Effective Entity Linking Algorithm.
Bibliographic record
Abstract
This paper reports on the participation of the LKD team in the English entity linking task at the TAC KBP 2013. We evaluated various modifications and combinations of the MostFrequent-Sense (MFS) based linking, the Entity Co-occurrence based linking (ECC), and the Explicit Semantic Analysis (ESA) based linking. We employed two our Wikipediabased NER systems, the Entityclassifier.eu and the SemiTags. Additionally, two Lucenebased entity linking systems were developed. For the competition we submitted 9 submissions in total, from which 5 used the textual context of the entities, and 4 submissions did not. Surprisingly, the MFS method based on the Wikipedia Search has proved to be the most effective approach – it achieved the best 0.555 B3+ F1 score from all our submissions and it achieved high 0.677 B3+ F1 score for Geo-Political (GPE) entities. In addition, the ESA based method achieved best 0.483 B3+ F1 for Organization (ORG) entities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".