Events are Not Simple: Identity, Non-Identity, and Quasi-Identity
Bibliographic record
Abstract
Abstract 1 Despite considerable theoretical and computational work on coreference, deciding when two entities or events are identical is very difficult. In a project to build corpora containing coreference links between events, we have identified three levels of event identity (full, partial, and none). Event coreference annotation on two corpora was performed to validate the findings. 1 The Problem of Identity Last year we had HLT in Montreal, and this year we did it in Atlanta. Does the “did it ” refer to the same conference or a different one? The two conferences are not identical, of course, but they are also not totally unrelated—else the “did it ” would not be interpretable. When creating text, we treat instances of entities and events as if they are fixed, well-described, and well-understood. When we say “that boat over there ” or “Mary’s wedding next month”, we assume the reader creates a mental representation of the referent, and we proceed to refer to it without further thought. However, as has been often noted in theoretical studies of semantics, this assumption is very problematic (Mill, 1872; Frege 1892; Guarino, 1999). Entities and (even more so) events are complex composite phenomena in the world, and they undergo change.
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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".