{"id":"W2169943035","doi":"10.48550/arxiv.1302.4813","title":"Probabilistic Frame Induction","year":2013,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Merge (version control); Parsing; Probabilistic logic; Artificial intelligence; Frame (networking); Natural language processing; Natural language; Event (particle physics); Set (abstract data type); Information retrieval; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007570467,0.00009740617,0.00008278147,0.0001061909,0.00009285074,0.0001055152,0.0007506252,0.00008351252,0.0000484273],"category_scores_gemma":[0.00005384194,0.00009553895,0.00003731902,0.0006024745,0.00005812852,0.001183923,0.0002135197,0.0001724683,0.0001978025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008108814,"about_ca_system_score_gemma":0.00003333511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001274418,"about_ca_topic_score_gemma":0.000004581339,"domain_scores_codex":[0.9992763,0.00003826735,0.00007271065,0.0003762199,0.00004849194,0.0001879422],"domain_scores_gemma":[0.9992676,0.00003347548,0.00006041317,0.0004409909,0.0001230426,0.00007452843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000002404364,0.00004667957,0.0004807237,0.00001712901,0.000008655774,0.00003678465,0.0001258204,0.0004283075,0.001551352,0.9877737,0.0009236669,0.008604747],"study_design_scores_gemma":[0.0001384229,0.00005521921,0.0005848146,0.00002162293,0.000007489366,0.00001256384,0.00004206762,0.1263958,0.002044079,0.8702366,0.0002243489,0.0002369224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2180785,0.00005996531,0.7797108,0.0002239794,0.0001251031,0.0001817194,2.981302e-7,0.0007767077,0.0008430021],"genre_scores_gemma":[0.9509034,0.000004589574,0.04806099,0.0001274647,0.00002715768,0.000001037705,7.992045e-7,0.000005340648,0.0008692674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7328249,"threshold_uncertainty_score":0.3895965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03869690680517766,"score_gpt":0.1776629811682244,"score_spread":0.1389660743630467,"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."}}