{"id":"W2356597680","doi":"","title":"Named entity recognition in Chinese medical records based on cascaded conditional random field","year":2014,"lang":"en","type":"article","venue":"Journal of Jilin University","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Conditional random field; CRFS; Feature (linguistics); Named-entity recognition; Context (archaeology); Computer science; Sentence; Word (group theory); Pattern recognition (psychology); Artificial intelligence; Layer (electronics); Natural language processing; Field (mathematics); Speech recognition; Mathematics; Engineering; Linguistics","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":[0.002123419,0.0008858155,0.0009356153,0.00264907,0.0006144793,0.0005474052,0.001452444,0.0007763577,0.001792629],"category_scores_gemma":[0.004151581,0.0003749075,0.001504722,0.002224256,0.0003018691,0.002034542,0.0006997561,0.0007877462,0.0007548759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006750186,"about_ca_system_score_gemma":0.001489929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01606014,"about_ca_topic_score_gemma":0.01448228,"domain_scores_codex":[0.9985751,0.0002778662,0.000155009,0.0005780193,0.0002963537,0.000117634],"domain_scores_gemma":[0.9974234,0.001454414,0.0002636988,0.0003368516,0.0004469013,0.00007471545],"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":[0.0009063643,0.0003086897,0.02619238,0.0007241499,0.0003529548,0.002017396,0.0006694928,0.08506537,0.03799159,0.005819635,0.02170817,0.8182439],"study_design_scores_gemma":[0.00004769451,0.0001451652,0.01463888,0.00003472316,0.0001843592,0.000895915,0.00008293492,0.956874,0.01817259,0.003967002,0.004864288,0.00009241865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07221107,0.001001889,0.9157824,0.000387122,0.0001485447,0.0003595511,0.002716833,0.006225971,0.001166661],"genre_scores_gemma":[0.5664101,0.0008621837,0.4209205,0.0001569791,0.0002022775,0.0003143615,0.008249048,0.0001517076,0.002732903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01606014,"threshold_uncertainty_score":0.03193331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021647753015759,"score_gpt":0.2238601073779896,"score_spread":0.213643629847832,"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."}}