{"id":"W4385567143","doi":"10.18653/v1/2022.finnlp-1.10","title":"A Taxonomical NLP Blueprint to Support Financial Decision Making through Information-Centred Interactions","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Blueprint; Usability; Taxonomy (biology); Visualization; Artificial intelligence; Data science; Software; Natural language processing; Human–computer interaction","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001313402,0.0001106733,0.0001476544,0.0002178018,0.0003068736,0.0001477186,0.00091034,0.00001940422,0.0008141013],"category_scores_gemma":[0.0001428506,0.0001126317,0.000103752,0.0006412324,0.00001220787,0.001823809,0.001762613,0.0002035723,0.000272936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003056858,"about_ca_system_score_gemma":0.000102734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002471046,"about_ca_topic_score_gemma":0.00004307482,"domain_scores_codex":[0.9987724,0.00003099977,0.0003976578,0.0002628504,0.0003082858,0.0002278068],"domain_scores_gemma":[0.9991111,0.0001009911,0.0001144514,0.0005469343,0.00006862701,0.00005787082],"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":[0.00005203935,0.0001795977,0.0004139132,0.000003063222,0.00001897989,0.00001407084,0.003201499,0.01091854,0.0002388804,0.2627024,0.05368868,0.6685684],"study_design_scores_gemma":[0.0003013356,0.0002059073,0.001309111,0.00001399386,0.000009615773,0.00005447694,0.0004157637,0.05733023,0.001936971,0.04139081,0.8966064,0.0004254053],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008588688,0.000002029827,0.9845437,0.000783952,0.0002522577,0.0002248927,0.000004209942,0.0003728165,0.005227473],"genre_scores_gemma":[0.5470648,7.33672e-7,0.4508575,0.001887355,0.00001697507,0.00009464241,0.000003704467,0.000002821852,0.00007141424],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8429177,"threshold_uncertainty_score":0.8913838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02622003112808004,"score_gpt":0.3076132401009309,"score_spread":0.2813932089728509,"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."}}