{"id":"W3199446243","doi":"10.1109/tnnls.2021.3112045","title":"Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Topic Modeling","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Interpretability; Hypersphere; Artificial intelligence; Natural language processing; Topic model; Leverage (statistics); Discriminative model; Word (group theory); Probabilistic logic; Graph; Set (abstract data type); Machine learning; Theoretical computer science; Mathematics","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":[0.001254484,0.001230671,0.000950423,0.002877353,0.0004062302,0.001251949,0.00143267,0.001325182,0.001421965],"category_scores_gemma":[0.006194836,0.0005116735,0.001592235,0.003943475,0.0008794696,0.004191493,0.001545525,0.00183577,0.001228663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008959805,"about_ca_system_score_gemma":0.0009189743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005893331,"about_ca_topic_score_gemma":0.009244579,"domain_scores_codex":[0.9989125,0.0003870559,0.00007299087,0.0003725193,0.0001824996,0.00007237936],"domain_scores_gemma":[0.9979278,0.00117786,0.0002455713,0.000304679,0.000266952,0.00007720276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002952173,0.0002051817,0.004849191,0.0004255845,0.0003162496,0.0003229875,0.0007883517,0.4674712,0.00871652,0.1043853,0.009986619,0.4022376],"study_design_scores_gemma":[0.00001046162,0.00002840723,0.0004768977,0.00001556457,0.00002886878,0.00006973316,0.00003661233,0.9494815,0.0005979433,0.04722265,0.002013454,0.00001780474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01265746,0.0006541691,0.9847749,0.0002332399,0.00003189555,0.00004372546,0.0003706037,0.0005030474,0.0007309474],"genre_scores_gemma":[0.6079547,0.002906876,0.3768208,0.0004957289,0.0003043734,0.0004696982,0.005793536,0.0004767312,0.004777528],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005893331,"threshold_uncertainty_score":0.01171803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02569691609597778,"score_gpt":0.2534840215965892,"score_spread":0.2277871055006114,"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."}}