{"id":"W108071511","doi":"","title":"A Novel Discriminative Framework for Sentence-Level Discourse Analysis","year":2012,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Discriminative model; Conditional random field; Computer science; Parsing; Sentence; Artificial intelligence; Natural language processing; Probabilistic logic; Classifier (UML); Margin (machine learning); Binary number; CRFS; Speech recognition; Machine learning; Mathematics","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":[0.0002962937,0.0001440184,0.0001953684,0.0002042813,0.0001132964,0.0001314249,0.0008130245,0.00007940397,0.00001714811],"category_scores_gemma":[0.0002455022,0.0001011584,0.000164867,0.0009785922,0.00005790651,0.0010472,0.0002514525,0.000134295,0.000005977962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004190353,"about_ca_system_score_gemma":0.00002414312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005465559,"about_ca_topic_score_gemma":0.0000139364,"domain_scores_codex":[0.9989164,0.00001709194,0.0001585667,0.0002876741,0.0002244915,0.0003957478],"domain_scores_gemma":[0.998979,0.0002229468,0.00009667697,0.0004625319,0.0001225374,0.0001162591],"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":[0.000003169587,0.000126591,0.000751172,0.0000104097,0.0001067588,4.638856e-7,0.002189566,0.000001374777,0.0007307675,0.9740108,0.000283755,0.02178515],"study_design_scores_gemma":[0.0005992233,0.000196105,0.009595049,0.0001511821,0.001006704,0.00003140039,0.002745271,0.08739549,0.1128021,0.7830714,0.0006834785,0.001722608],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004971028,0.0003639766,0.9964956,0.001637184,0.0001462313,0.0001766172,0.00001617862,0.000378835,0.0002882593],"genre_scores_gemma":[0.4266846,0.00000109669,0.5725853,0.0002892234,0.00005929057,0.00003604329,0.000003649053,0.000004777682,0.0003359358],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4261875,"threshold_uncertainty_score":0.412512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06085311550217667,"score_gpt":0.3646154715102523,"score_spread":0.3037623560080756,"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."}}