{"id":"W2109753595","doi":"10.1109/lsp.2010.2048940","title":"Self-Organizing Maps for Topic Trend Discovery","year":2010,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Mitacs","keywords":"Latent Dirichlet allocation; Computer science; Topic model; Visualization; Dimensionality reduction; Data mining; Information retrieval; Set (abstract data type); Data visualization; Process (computing); Latent semantic analysis; Curse of dimensionality; Artificial intelligence; Machine learning","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":[0.001784859,0.0009126985,0.0009175303,0.005849938,0.0009168875,0.001900693,0.001249451,0.0009607089,0.002899342],"category_scores_gemma":[0.008497552,0.0005529106,0.001147558,0.005019304,0.0004834977,0.001847733,0.00121531,0.001030481,0.001325697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006842669,"about_ca_system_score_gemma":0.0009030539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004405148,"about_ca_topic_score_gemma":0.004498892,"domain_scores_codex":[0.9989721,0.0003932917,0.00005543385,0.0001978932,0.000303558,0.00007780097],"domain_scores_gemma":[0.9968953,0.001818161,0.0002910942,0.0004019385,0.000512248,0.00008134],"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":[0.0003355887,0.0002000733,0.005722644,0.0005551165,0.0003877735,0.0004312099,0.001026341,0.212314,0.00548469,0.05372649,0.03075402,0.6890621],"study_design_scores_gemma":[0.00001666767,0.00002104377,0.001467119,0.00002511574,0.00002418199,0.0001013023,0.0001565724,0.9414755,0.001938749,0.04663886,0.008096945,0.00003792335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01885403,0.001249539,0.97082,0.0004187914,0.0001365505,0.0001406884,0.001397489,0.004232716,0.002750206],"genre_scores_gemma":[0.2876265,0.001143065,0.7032385,0.0001074763,0.0003182381,0.0006402695,0.00314243,0.0004510318,0.003332358],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005849938,"threshold_uncertainty_score":0.009699285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01012035866637828,"score_gpt":0.2520302530256199,"score_spread":0.2419098943592417,"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."}}