Unsupervised Coreference Resolution with HyperGraph Partitioning
Why this work is in the frame
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Bibliographic record
Abstract
Unsupervised-learning based coreference resolution obviates the need for annotation of training data. However, unsupervised approaches have traditionally been relying on the use of mention-pair models, which only consider information pertaining to a pair of mentions at a time. In this paper, it is proposed the use of hypergraph partitioning to overcome this limitation. The mentions are modeled as vertices. By allowing a hyperedge to cover multiple mentions that share a common property, the additional information beyond a mention pair can be captured. This paper introduces a hypergraph partitioning algorithm that divides mentions directly into equivalence classes representing individual entities. Evaluation on the ACE dataset shows that our unsupervised hypergraph based approach outperforms previous unsupervised methods.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.013 |
| Open science | 0.001 | 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