{"id":"W2951586423","doi":"10.1111/coin.12225","title":"Multi‐representational convolutional neural networks for text classification","year":2019,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"Natural Science Foundation of Tianjin City; National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Natural language processing; Embedding; Focus (optics); Word embedding; Word (group theory); Semantics (computer science); Categorization; Domain (mathematical analysis); Pattern recognition (psychology)","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":[0.000243366,0.0001404018,0.0001309454,0.0001018836,0.0001372933,0.0001349544,0.0007093078,0.00006823122,0.00007081683],"category_scores_gemma":[0.00007350522,0.000149218,0.00009641964,0.0002462115,0.00005493527,0.0005029435,0.0001279331,0.0001304616,0.0001641141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007703783,"about_ca_system_score_gemma":0.0001078196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000077812,"about_ca_topic_score_gemma":0.000001629322,"domain_scores_codex":[0.9984044,0.00004720628,0.0003847682,0.0005570806,0.0003552179,0.000251348],"domain_scores_gemma":[0.9983171,0.0007128493,0.0001409715,0.0003076018,0.0004403397,0.00008120373],"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":[0.000006405781,0.00003170178,0.00175002,0.000006297154,0.000008681892,3.334352e-7,0.00006584331,0.7127671,0.00004731528,0.273244,0.0001414641,0.01193081],"study_design_scores_gemma":[0.0001712396,0.0000292972,0.01549201,0.000008581899,0.000002565089,0.00001221562,0.00003120987,0.9582679,0.00003925576,0.02552499,0.0002586147,0.0001621129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005230157,0.0001180024,0.9919708,0.000968979,0.0009274548,0.0004606668,0.000007787346,0.000119688,0.0001964408],"genre_scores_gemma":[0.6951298,0.000001814038,0.3039733,0.0003945711,0.0001107474,0.00004987748,0.00006851488,0.00000739437,0.0002640305],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6898996,"threshold_uncertainty_score":0.6084934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0945598607666244,"score_gpt":0.3378425082975396,"score_spread":0.2432826475309152,"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."}}