A Machine Learning Filter for the Slot Filling Task
Bibliographic record
Abstract
Slot Filling, a subtask of Relation Extraction, represents a key aspect for building structured knowledge bases usable for semantic-based information retrieval. In this work, we present a machine learning filter whose aim is to enhance the precision of relation extractors while minimizing the impact on the recall. Our approach consists in the filtering of relation extractors’ output using a binary classifier. This classifier is based on a wide array of features including syntactic, semantic and statistical features such as the most frequent part-of-speech patterns or the syntactic dependencies between entities. We experimented the classifier on the 18 participating systems in the TAC KBP 2013 English Slot Filling track. The TAC KBP English Slot Filling track is an evaluation campaign that targets the extraction of 41 pre-identified relations (e.g., title, date of birth, countries of residence, etc.) related to specific named entities (persons and organizations). Our results show that the classifier is able to improve the global precision of the best 2013 system by 20.5% and improve the F1-score for 20 relations out of 33 considered.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".