{"id":"W1625323","doi":"10.1007/bf00233285","title":"Using LTAG-Based Features for Semantic Role Labeling","year":2006,"lang":"en","type":"article","venue":"The Journal of Membrane Biology","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Semantic role labeling; Discriminative model; Artificial intelligence; Natural language processing; Parsing; Task (project management); Tree (set theory); Grammar; Decision tree; Mathematics; Linguistics; Sentence","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":[0.0004888335,0.0006255804,0.000377691,0.0008300518,0.0003895582,0.001297218,0.0009289688,0.0009283206,0.007605901],"category_scores_gemma":[0.000754106,0.0003894419,0.0005660022,0.001193583,0.0004190361,0.001860118,0.001087157,0.00124127,0.008531567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007252601,"about_ca_system_score_gemma":0.0004879736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001412587,"about_ca_topic_score_gemma":0.001454121,"domain_scores_codex":[0.9996177,0.00004925569,0.00003675851,0.0001279178,0.00009590363,0.00007244261],"domain_scores_gemma":[0.9994774,0.00009266496,0.00005371513,0.0002079021,0.0001291566,0.00003912091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007903033,0.0001805903,0.001890158,0.0005616787,0.00004575856,0.0008211682,0.0007966416,0.00374978,0.6556531,0.05381752,0.02253479,0.2591586],"study_design_scores_gemma":[0.00007296996,0.0003566752,0.003195773,0.00008706085,0.00007562146,0.001087412,0.0003176618,0.1141857,0.3959075,0.04657193,0.4380644,0.00007730853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02568213,0.0001914055,0.9244281,0.0001860738,0.000183693,0.0002006389,0.003877381,0.03686304,0.008387596],"genre_scores_gemma":[0.2133102,0.0003077333,0.7494063,0.0003334929,0.00007087721,0.0005899322,0.01898454,0.005763112,0.01123364],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007605901,"threshold_uncertainty_score":0.02544433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01774372132035696,"score_gpt":0.2973177865186044,"score_spread":0.2795740651982475,"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."}}