{"id":"W4386952323","doi":"10.1109/iceccme57830.2023.10252703","title":"BenCo: First Step Towards Coreference Resolution in Bengali","year":2023,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Bengali; Coreference; Computer science; Natural language processing; Artificial intelligence; Resolution (logic); Domain (mathematical analysis); Language understanding","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":[0.001598157,0.001368341,0.0009844712,0.002904523,0.003241976,0.0025958,0.002178064,0.001481799,0.007299809],"category_scores_gemma":[0.00744065,0.0004430748,0.0008983877,0.002676985,0.0008065585,0.002449207,0.003953386,0.001739445,0.00738231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002372941,"about_ca_system_score_gemma":0.002133022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07306492,"about_ca_topic_score_gemma":0.1308638,"domain_scores_codex":[0.9967346,0.001073951,0.0002085149,0.001149812,0.0004592636,0.0003739007],"domain_scores_gemma":[0.9972038,0.0007442973,0.0001113377,0.001003064,0.0007930686,0.0001444888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002240672,0.000508018,0.02361946,0.001919569,0.0005887186,0.002113018,0.008892196,0.01327994,0.09467763,0.01189564,0.1533175,0.6869477],"study_design_scores_gemma":[0.0001814641,0.0003173162,0.04296195,0.0003456722,0.0004030864,0.00174564,0.0116843,0.160184,0.2145634,0.01678591,0.5505507,0.0002764086],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4908645,0.006871928,0.260399,0.004034439,0.001362272,0.001306416,0.07380388,0.08141299,0.07994453],"genre_scores_gemma":[0.5974825,0.0009512627,0.2466674,0.0007950577,0.0001345857,0.0006380805,0.1240577,0.002709371,0.02656399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07306492,"threshold_uncertainty_score":0.1452793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03114101348090963,"score_gpt":0.2900226778401264,"score_spread":0.2588816643592167,"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."}}