{"id":"W4313021298","doi":"10.23952/jano.4.2022.3.06","title":"Hierarchical reinforcement learning with advantage function for entity relation extraction","year":2022,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Relation (database); Relationship extraction; Reinforcement; Computer science; Function (biology); Extraction (chemistry); Artificial intelligence; Psychology; Data mining; Biology; Chemistry; Social psychology; Chromatography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.002061117,0.0009214442,0.001290904,0.0007096521,0.0004063431,0.0007348778,0.001361348,0.001069413,0.002480783],"category_scores_gemma":[0.006478291,0.0004888367,0.0006900922,0.0005585295,0.0009289932,0.002046784,0.001332308,0.0018567,0.0004686865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162148,"about_ca_system_score_gemma":0.001835109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005097711,"about_ca_topic_score_gemma":0.005001012,"domain_scores_codex":[0.9988344,0.0003548074,0.0000786724,0.0003140144,0.0003023738,0.00011572],"domain_scores_gemma":[0.9975023,0.001669775,0.0002041505,0.0001929019,0.0003301741,0.0001007226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002529596,0.0002155699,0.002164593,0.0001765696,0.0001054524,0.0001989839,0.0001955734,0.7409509,0.005653067,0.02162859,0.002495245,0.2259624],"study_design_scores_gemma":[0.0000105584,0.00002085949,0.00008931,0.000002853489,0.000006225541,0.00001000765,0.000002563815,0.9958541,0.0005333651,0.003211115,0.0002536223,0.000005435044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01346723,0.0002779237,0.9838787,0.0001960081,0.00003549242,0.000058581,0.00004185853,0.0009486859,0.001095433],"genre_scores_gemma":[0.7728329,0.0002409132,0.221651,0.000346599,0.000056114,0.0002364264,0.0002249643,0.000145236,0.004265888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005097711,"threshold_uncertainty_score":0.01090032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008532871366860002,"score_gpt":0.2314240908626318,"score_spread":0.2228912194957718,"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."}}