{"id":"W4318776785","doi":"10.3390/data8020034","title":"Neural Coreference Resolution for Dutch Parliamentary Documents with the DutchParliament Dataset","year":2023,"lang":"en","type":"article","venue":"Data","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Canadian Institute of Steel Construction","keywords":"Coreference; Computer science; Natural language processing; Artificial intelligence; Task (project management); Resolution (logic); Metadata; Annotation; Cluster analysis; Information retrieval; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002204805,0.00125444,0.0008282642,0.003170518,0.00228117,0.001129362,0.002397314,0.001973372,0.007767016],"category_scores_gemma":[0.008530172,0.0003542354,0.001194951,0.00402044,0.0007091681,0.001843588,0.001964012,0.001569211,0.005618265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002100764,"about_ca_system_score_gemma":0.002256618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05039258,"about_ca_topic_score_gemma":0.1034141,"domain_scores_codex":[0.9971468,0.0007780021,0.0002882748,0.0009394966,0.0006197104,0.0002278389],"domain_scores_gemma":[0.9971849,0.0009203806,0.0002064987,0.0008219732,0.0007396952,0.0001264351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00184826,0.0008390049,0.01368745,0.002966106,0.000497305,0.002526313,0.002335945,0.02023696,0.02108995,0.008037458,0.605945,0.3199903],"study_design_scores_gemma":[0.0006416556,0.0003024731,0.04213128,0.0004253286,0.000274048,0.002223929,0.003545532,0.1272214,0.03983228,0.007079757,0.7760644,0.0002578863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3183012,0.007626519,0.06787196,0.001828832,0.001248167,0.001878523,0.5303452,0.01239603,0.05850358],"genre_scores_gemma":[0.1064353,0.0004759914,0.0457412,0.0002509207,0.00006543515,0.0009327666,0.8373693,0.0003767922,0.008352206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05039258,"threshold_uncertainty_score":0.1001985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06760614956412057,"score_gpt":0.3378913798641697,"score_spread":0.2702852303000491,"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."}}