{"id":"W2807312676","doi":"10.63317/2vxw58tvfbm4","title":"Integrating Generative Lexicon Event Structures into VerbNet","year":2018,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Generative grammar; Lexicon; Event (particle physics); Artificial intelligence; Natural language processing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001453337,0.000902083,0.000709182,0.002542961,0.0008112813,0.003755862,0.001510582,0.0009179324,0.01555305],"category_scores_gemma":[0.005857869,0.0009236024,0.00105988,0.002198859,0.0007672459,0.006408817,0.00226382,0.001681201,0.005248803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001335344,"about_ca_system_score_gemma":0.001544823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005477451,"about_ca_topic_score_gemma":0.01356708,"domain_scores_codex":[0.99897,0.0003258515,0.00008766898,0.0003108215,0.000227621,0.00007814152],"domain_scores_gemma":[0.9979987,0.001063849,0.0001010051,0.0003810042,0.0003818611,0.00007355735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005150776,0.000338462,0.003440379,0.0006358607,0.0001904731,0.0006340071,0.001182723,0.04682715,0.01672539,0.3493925,0.02585275,0.5542653],"study_design_scores_gemma":[0.00005908814,0.00005546738,0.0008440541,0.0001172016,0.0001264359,0.0001925521,0.0003131964,0.5041172,0.0136202,0.4384143,0.04207905,0.00006136359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0124722,0.0002075589,0.9618267,0.000425377,0.000218782,0.0001463905,0.001795703,0.01262861,0.01027868],"genre_scores_gemma":[0.4325759,0.0005026305,0.5442528,0.0003083788,0.0001968196,0.0001854296,0.009223984,0.003553482,0.009200645],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01555305,"threshold_uncertainty_score":0.05203015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134297152447872,"score_gpt":0.3045055836957543,"score_spread":0.2931626121712755,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}