Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".