{"id":"W4389520223","doi":"10.18653/v1/2023.emnlp-tutorial.1","title":"NLP+Vis: NLP Meets Visualization","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Leverage (statistics); Visualization; Artificial intelligence; Intersection (aeronautics); Natural language processing; Modalities; Focus (optics); Deep learning","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":[0.003762978,0.002385554,0.001110893,0.002726145,0.001647015,0.01038047,0.002551764,0.003672633,0.05446575],"category_scores_gemma":[0.01705807,0.001214993,0.002316715,0.003175196,0.00332859,0.02179835,0.006514115,0.007207941,0.01617014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001713836,"about_ca_system_score_gemma":0.002007791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002383393,"about_ca_topic_score_gemma":0.002820439,"domain_scores_codex":[0.9963192,0.001956957,0.000296848,0.0005224955,0.0007390343,0.0001656153],"domain_scores_gemma":[0.993395,0.004439518,0.000242344,0.0009511336,0.000657521,0.0003145168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001051626,0.00004162279,0.000351282,0.001944526,0.00007488159,0.0003671313,0.001736998,0.004324106,0.004717549,0.4846113,0.2006956,0.3010299],"study_design_scores_gemma":[0.00001652127,0.00001828903,0.0002009503,0.0004871042,0.00001767015,0.0005295166,0.0003579347,0.01801413,0.002289159,0.3206975,0.657324,0.00004727776],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008920563,0.007674339,0.9332482,0.009213767,0.001566587,0.0002048865,0.001729122,0.01220981,0.03326124],"genre_scores_gemma":[0.03047671,0.01784534,0.909669,0.003704079,0.002191988,0.0008013179,0.005004548,0.007017069,0.02328999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05446575,"threshold_uncertainty_score":0.1822061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03612026689251242,"score_gpt":0.3393710046578898,"score_spread":0.3032507377653774,"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."}}