Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Entity resolution (ER) approaches typically consist of a blocker and a matcher. They share the same goal and cooperate in different roles: the blocker first quickly removes obvious non-matches, and the matcher subsequently determines whether the remaining pairs refer to the same real-world entity. Despite the state-of-the-art performance achieved by deep learning methods in ER, these techniques often rely on a large amount of labeled data for training, which can be challenging or costly to obtain. Thus, there is a need to develop effective ER systems under low-resource settings. In this work, we propose an end-to-end iterative Co-learning framework for ER, aimed at jointly training the blocker and the matcher by leveraging their cooperative relationship. In particular, we let the blocker and the matcher share their learned knowledge with each other via iteratively updated pseudo labels, which broaden the supervision signals. To mitigate the impact of noise in pseudo labels, we develop optimization techniques from three aspects: label generation, label selection and model training. Through extensive experiments on benchmark datasets, we demonstrate that our proposed framework outperforms baselines by an average of 9.13--51.55%. Furthermore, our analysis confirms that our framework achieves mutual benefits between the blocker and the matcher.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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