{"id":"W1902044186","doi":"10.3917/i2d.152.0070","title":"L’extraction d’entités nommées : une opportunité pour le secteur culturel ?","year":2015,"lang":"fr","type":"article","venue":"I2D - Information données & documents","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.008309878,0.0008598091,0.0005942027,0.003282758,0.00336085,0.008255018,0.001725752,0.001548544,0.008575985],"category_scores_gemma":[0.01748878,0.0006389776,0.0009202268,0.004438773,0.003418018,0.0117807,0.00297504,0.001771945,0.002962953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005622158,"about_ca_system_score_gemma":0.007541494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09338818,"about_ca_topic_score_gemma":0.1252769,"domain_scores_codex":[0.9950777,0.001964841,0.0003461459,0.0006402617,0.001730297,0.0002406119],"domain_scores_gemma":[0.9858548,0.005506787,0.0006660811,0.002695393,0.00505143,0.000225545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002535586,0.00009268858,0.02395676,0.002231687,0.000189309,0.001774044,0.08050966,0.002991267,0.04206034,0.1463833,0.02743958,0.6721179],"study_design_scores_gemma":[0.00002741854,0.0001047618,0.02577547,0.001289293,0.0001892715,0.00218163,0.04108341,0.01266005,0.04352738,0.05194258,0.8209918,0.0002268494],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1913691,0.005527264,0.662344,0.01886772,0.0005490207,0.0007103907,0.002954014,0.003950524,0.1137279],"genre_scores_gemma":[0.4531643,0.004024985,0.4712287,0.002017779,0.0001242586,0.0004150911,0.004744675,0.001726083,0.06255415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09338818,"threshold_uncertainty_score":0.1856892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04627228685610568,"score_gpt":0.3064324269789142,"score_spread":0.2601601401228085,"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."}}