{"id":"W2061282927","doi":"10.1145/2494266.2494280","title":"A graph-based topic extraction method enabling simple interactive customization","year":2013,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Boeing","keywords":"Computer science; Interpretability; Latent Dirichlet allocation; Cluster analysis; Personalization; Artificial intelligence; Graph; Data mining; Machine learning; Representation (politics); Matrix decomposition; Set (abstract data type); Topic model; Information retrieval; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001126616,0.001043711,0.0008215749,0.00430319,0.00094953,0.001435535,0.0009568849,0.0009245636,0.004254159],"category_scores_gemma":[0.004542274,0.0006383069,0.001365756,0.003774621,0.0005821269,0.002020094,0.001199082,0.001169085,0.002949996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005154843,"about_ca_system_score_gemma":0.001052083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002762221,"about_ca_topic_score_gemma":0.004214411,"domain_scores_codex":[0.9988227,0.0003092283,0.00009267759,0.0003808593,0.0003295622,0.00006497262],"domain_scores_gemma":[0.9981244,0.000937182,0.000137418,0.0002983175,0.0004394013,0.00006319524],"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":[0.0003234361,0.0001333845,0.001496725,0.0004956336,0.0001565688,0.0002989515,0.0007728823,0.02120531,0.07412442,0.01885838,0.01870673,0.8634276],"study_design_scores_gemma":[0.0001518216,0.0001497831,0.004143944,0.0001006965,0.0001967386,0.001558416,0.0004629815,0.7765829,0.06573034,0.05388024,0.09683139,0.0002108657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003372846,0.0001123408,0.9919853,0.00008329241,0.00003207248,0.0001092312,0.0002786175,0.003447771,0.0005785135],"genre_scores_gemma":[0.04027629,0.0002008656,0.9550279,0.00005797532,0.00005308215,0.0002986032,0.001425944,0.0005843279,0.002074977],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00430319,"threshold_uncertainty_score":0.01423156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02366903509246583,"score_gpt":0.3098053513791356,"score_spread":0.2861363162866698,"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."}}