{"id":"W4211156271","doi":"10.2200/s00239ed1v01y200912hlt006","title":"Semantic Role Labeling","year":2011,"lang":"en","type":"article","venue":"Institutional Research Information System (Università degli Studi di Trento)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"FrameNet; Computer science; Semantic role labeling; Natural language processing; Artificial intelligence; Parsing; Task (project management); Inference; Annotation; Context (archaeology)","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":[0.003146322,0.001420016,0.0008686557,0.003028723,0.002915093,0.005205999,0.00332378,0.002337921,0.0457732],"category_scores_gemma":[0.006784969,0.0008984272,0.001313416,0.002742163,0.003014517,0.01356034,0.003396742,0.003510629,0.02315488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002975675,"about_ca_system_score_gemma":0.002370837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002066435,"about_ca_topic_score_gemma":0.002460039,"domain_scores_codex":[0.9970436,0.001002913,0.0002276166,0.0006719654,0.000813177,0.0002406875],"domain_scores_gemma":[0.9975489,0.0010165,0.0001052713,0.0006934041,0.0005327436,0.0001032393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003754634,0.00002591855,0.0001760808,0.0002339081,0.000008804242,0.0001107205,0.000867035,0.0007031093,0.001183144,0.8018875,0.07615294,0.1186133],"study_design_scores_gemma":[0.000006153306,0.000009907033,0.000125284,0.0002080066,0.000008973608,0.0003114982,0.0004106024,0.003449487,0.001637043,0.2708119,0.7229989,0.00002230331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001945139,0.003881793,0.6940091,0.00653988,0.00258383,0.0003692558,0.0025917,0.004825083,0.2832543],"genre_scores_gemma":[0.0904789,0.008295106,0.7288821,0.004064563,0.00190014,0.0008285338,0.01255005,0.004008784,0.1489918],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0457732,"threshold_uncertainty_score":0.1531267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07674148792415966,"score_gpt":0.3072401343124997,"score_spread":0.23049864638834,"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."}}