{"id":"W4389518321","doi":"10.18653/v1/2023.arabicnlp-1.25","title":"Arabic Fine-Grained Entity Recognition","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital","funders":"","keywords":"Computer science; Natural language processing; Named-entity recognition; Arabic; Artificial intelligence; Entity linking; Modern Standard Arabic; Information retrieval; Linguistics; Knowledge base","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":[0.00147032,0.002018644,0.0008458759,0.002913857,0.001233771,0.001616982,0.001408746,0.001140518,0.02197514],"category_scores_gemma":[0.007600793,0.0004400277,0.0008338363,0.001882029,0.0006103893,0.004560053,0.002856185,0.001839803,0.01718617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000907724,"about_ca_system_score_gemma":0.001143286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00851645,"about_ca_topic_score_gemma":0.01313211,"domain_scores_codex":[0.9984404,0.0002954951,0.0001982859,0.0006452863,0.0002964796,0.0001240578],"domain_scores_gemma":[0.9959929,0.001045087,0.0001780962,0.001068268,0.001585751,0.0001298144],"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":[0.0008728896,0.0001699698,0.005386044,0.002194082,0.0001436513,0.001691304,0.00116593,0.01432502,0.05440681,0.01259019,0.2256479,0.6814062],"study_design_scores_gemma":[0.0001497289,0.0002204506,0.01804762,0.0007173399,0.0001921635,0.002954461,0.001903017,0.2324881,0.1378618,0.01880379,0.5862534,0.000408104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1967096,0.006162365,0.4759842,0.002756874,0.003499259,0.0009662961,0.1026818,0.1237028,0.08753683],"genre_scores_gemma":[0.3652763,0.001607691,0.40215,0.001245984,0.0002732535,0.0006195872,0.1849736,0.003188805,0.04066473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02197514,"threshold_uncertainty_score":0.0735141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0572780891113574,"score_gpt":0.2594864063940251,"score_spread":0.2022083172826677,"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."}}