{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001072319,0.00006649765,0.0001705888,0.0002314723,0.00005580437,0.00001719369,0.0004310819,0.00009567068,0.0001662169],"category_scores_gemma":[0.0008216596,0.00006043262,0.00009398291,0.0001841337,0.00002103897,0.0003102885,0.00004985801,0.0003647414,0.000007422846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009635365,"about_ca_system_score_gemma":0.0001630875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000492953,"about_ca_topic_score_gemma":0.0001064005,"domain_scores_codex":[0.9988694,0.0002445255,0.0002115418,0.0001274916,0.0004431194,0.0001038804],"domain_scores_gemma":[0.9989284,0.0005283408,0.0001675621,0.0001350967,0.0001230506,0.0001175417],"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":[0.0111446,0.004550736,0.3486703,0.0003005915,0.0003194632,0.006119145,0.003487551,0.04338458,0.001290919,0.02005959,0.01769267,0.5429798],"study_design_scores_gemma":[0.01094524,0.0004211052,0.03291341,0.0001992512,0.00001380658,0.0000831497,0.00004079582,0.9438366,0.0002287431,0.009086347,0.002033515,0.0001980654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5271112,0.000002526125,0.4695306,0.002114779,0.000374323,0.00003010978,0.000001214333,0.000008942944,0.0008263145],"genre_scores_gemma":[0.9907,0.000009250678,0.008488815,0.0006131002,0.0001355714,5.936017e-8,0.000002019822,0.00000172368,0.00004946889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.900452,"threshold_uncertainty_score":0.2464371,"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."}}