{"id":"W1587116172","doi":"10.1007/978-3-642-13059-5_32","title":"Automatic Text Segmentation for Movie Subtitles","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Text segmentation; Natural language processing; Information retrieval; Order (exchange); Image segmentation; Computer vision; Computer graphics (images)","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":[0.0002821225,0.001818128,0.001228086,0.005229231,0.001211383,0.001451728,0.001035863,0.0009718362,0.01956821],"category_scores_gemma":[0.001135751,0.000546383,0.001141276,0.003179339,0.0003094002,0.001378938,0.0007756375,0.0008368687,0.01476731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006635297,"about_ca_system_score_gemma":0.0009570326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009670514,"about_ca_topic_score_gemma":0.01893253,"domain_scores_codex":[0.9996253,0.00002806783,0.00003230603,0.0001522793,0.00008829909,0.00007376273],"domain_scores_gemma":[0.9990362,0.000268744,0.00008567076,0.00008584512,0.0004348264,0.00008882018],"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":[0.0007274895,0.0001197368,0.001669631,0.0009414593,0.00006912616,0.0003587699,0.0002541483,0.001051824,0.1675053,0.001384512,0.077488,0.7484301],"study_design_scores_gemma":[0.0002262854,0.0008291007,0.04423294,0.0005685518,0.0006121955,0.002601357,0.001806497,0.3257404,0.3937678,0.008491863,0.2209014,0.0002215934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1801013,0.01173311,0.590209,0.001162542,0.001797243,0.001783588,0.06491974,0.1062477,0.04204579],"genre_scores_gemma":[0.2087142,0.002384437,0.6434066,0.0002902358,0.0006895434,0.0007320127,0.1007746,0.004665386,0.03834298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01956821,"threshold_uncertainty_score":0.06546217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01334097530898132,"score_gpt":0.2748172718560952,"score_spread":0.2614762965471139,"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."}}