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Record W2507883388 · doi:10.18653/v1/w16-3005

VERSE: Event and Relation Extraction in the BioNLP 2016 Shared Task

2016· article· en· W2507883388 on OpenAlex
Jake Lever, Steven J.M. Jones

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsCanada's Michael Smith Genome Sciences Centre
Fundersnot available
KeywordsComputer scienceRelationship extractionBiomedical text miningTask (project management)Relation (database)Event (particle physics)Natural language processingInformation retrievalData miningText miningEngineeringPhysics

Abstract

fetched live from OpenAlex

We present the Vancouver Event and Relation System for Extraction (VERSE) 1 as a competing system for three subtasks of the BioNLP Shared Task 2016. VERSE performs full event extraction including entity, relation and modification extraction using a feature-based approach. It achieved the highest F1-score in the Bacteria Biotope (BB3) event subtask and the third highest F1-score in the Seed Development (SeeDev) binary subtask.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.083

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.283
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations39
Published2016
Admission routes2
Has abstractyes

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