{"id":"W2026130194","doi":"10.3115/1220575.1220686","title":"Exploiting a verb lexicon in automatic semantic role labelling","year":2005,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lexicon; Labelling; Computer science; Predicate (mathematical logic); Semantic role labeling; Natural language processing; Artificial intelligence; Verb; FrameNet; Set (abstract data type); Parsing; Programming language; Sentence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000288279,0.0001003539,0.000120916,0.0001520841,0.00005034436,0.0001542613,0.0005976855,0.0000490949,0.00002296849],"category_scores_gemma":[0.00005228434,0.0000878608,0.00002500498,0.0003756817,0.00001227935,0.0008170536,0.0001780583,0.000155242,0.00006160157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006373638,"about_ca_system_score_gemma":0.00003608538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000647608,"about_ca_topic_score_gemma":0.00006950573,"domain_scores_codex":[0.9990826,0.00003006039,0.0002137621,0.000248563,0.0001676066,0.0002574334],"domain_scores_gemma":[0.9995452,0.00006059758,0.00005493167,0.0002770992,0.00002503845,0.00003718425],"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":[0.000001326543,0.00008728525,0.0007417789,0.00007267834,0.000005407333,0.00004113063,0.003453916,0.0001315083,0.02476259,0.1130987,0.0001423504,0.8574613],"study_design_scores_gemma":[0.0001543537,0.00001850067,0.00005970427,0.0001642551,0.000001574056,0.00002019805,0.00006754678,0.8466399,0.1134105,0.03910815,0.0001637795,0.0001914907],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1704762,0.002352923,0.8191511,0.001897683,0.00005776246,0.0001711806,1.219109e-7,0.00246678,0.003426211],"genre_scores_gemma":[0.5341637,0.000003043838,0.4654205,0.0002803824,0.00002360618,0.000006297086,1.958567e-7,0.000004640664,0.00009766482],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8572698,"threshold_uncertainty_score":0.3582859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01126895950853088,"score_gpt":0.2598953051233368,"score_spread":0.2486263456148059,"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."}}