{"id":"W4312839873","doi":"10.1007/978-981-19-8746-5_11","title":"Hierarchical Topic Model Inference by Community Discovery on Word Co-occurrence Networks","year":2022,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke; University of Alberta","funders":"","keywords":"Topic model; Latent Dirichlet allocation; Computer science; Hierarchy; Inference; Set (abstract data type); Probabilistic logic; Graph; Community structure; Graphical model; Word (group theory); Artificial intelligence; Data science; Information retrieval; Natural language processing; Theoretical computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003570149,0.001134636,0.00209319,0.003687625,0.001171591,0.002278775,0.003244674,0.002043064,0.003110009],"category_scores_gemma":[0.01800203,0.001424076,0.002391519,0.005473704,0.001051955,0.004807973,0.002731976,0.003330106,0.002082661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001158724,"about_ca_system_score_gemma":0.001030592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007055743,"about_ca_topic_score_gemma":0.00926719,"domain_scores_codex":[0.9972493,0.001325576,0.0001357388,0.0007171738,0.0003936234,0.0001785964],"domain_scores_gemma":[0.9857691,0.0121482,0.000427312,0.0009070081,0.0005593185,0.000189064],"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":[0.0006480452,0.0003940377,0.007125721,0.0006761721,0.0008819324,0.0004831089,0.0009056003,0.3699554,0.00859667,0.09749811,0.02384987,0.4889853],"study_design_scores_gemma":[0.00001123374,0.00001053764,0.0003551638,0.0000144808,0.00003398138,0.00004893305,0.00002268454,0.9571869,0.0004804968,0.04070661,0.00111669,0.00001234911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009368832,0.0008679478,0.9877602,0.0002756732,0.00006370735,0.00004453661,0.0003557605,0.0005465419,0.0007168609],"genre_scores_gemma":[0.3155935,0.002152654,0.6661302,0.0003463489,0.0007300629,0.0005359961,0.004977405,0.0006374551,0.008896401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007055743,"threshold_uncertainty_score":0.01888102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.071469626240898,"score_gpt":0.3191911345546424,"score_spread":0.2477215083137444,"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."}}