{"id":"W4388731527","doi":"10.1017/s1351324923000529","title":"Korean named entity recognition based on language-specific features – CORRIGENDUM","year":2023,"lang":"en","type":"erratum","venue":"Natural Language Engineering","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Content (measure theory); Natural language processing; Information retrieval; Artificial intelligence; Database; World Wide Web","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":[0.0008945065,0.001088513,0.0008209739,0.001538975,0.000947396,0.002120643,0.001280158,0.001073141,0.07033081],"category_scores_gemma":[0.007221515,0.0004249924,0.0006416319,0.001745249,0.000541109,0.00180399,0.0009291676,0.001408819,0.07174063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030194,"about_ca_system_score_gemma":0.001078405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01239628,"about_ca_topic_score_gemma":0.01983834,"domain_scores_codex":[0.9992251,0.0001036288,0.0001480923,0.0001415743,0.0003330937,0.00004859775],"domain_scores_gemma":[0.9948537,0.0005056783,0.0001464901,0.0006305784,0.003737837,0.0001258131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004811326,0.00002003094,0.0001656575,0.0001472049,0.00001650534,0.0006597371,0.00002536788,0.0001824331,0.0008989096,0.001255639,0.9561004,0.04047994],"study_design_scores_gemma":[0.00001890944,0.00003758346,0.002116223,0.0001419429,0.00005504158,0.001108262,0.0001261945,0.002360163,0.005663846,0.002539647,0.9857772,0.00005501493],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.00818963,0.00768495,0.06627665,0.04189344,0.776669,0.0003066548,0.01526762,0.008511608,0.07520048],"genre_scores_gemma":[0.04932385,0.01089891,0.07350224,0.01769648,0.02871746,0.0002447429,0.0400966,0.005645645,0.773874],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.07033081,"threshold_uncertainty_score":0.23528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01251648226489134,"score_gpt":0.2419384875471892,"score_spread":0.2294220052822979,"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."}}