{"id":"W4295308551","doi":"10.1109/tai.2022.3205567","title":"DReD–A Descriptive Relation Dataset for Expanding Relation Extraction","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Relationship extraction; Computer science; Relation (database); Benchmark (surveying); Sentence; Natural language processing; Task (project management); Artificial intelligence; Code (set theory); Set (abstract data type); Information retrieval; Data mining","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":[0.0005662238,0.0001451093,0.0001217516,0.0002765469,0.001124302,0.0001264435,0.0004291126,0.00006140289,0.0001269495],"category_scores_gemma":[0.00002169822,0.0001746999,0.00009153839,0.0004937369,0.00003011167,0.0009306869,0.000007149415,0.0003865991,0.00006013045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003454731,"about_ca_system_score_gemma":0.0000670424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007549126,"about_ca_topic_score_gemma":0.00004506903,"domain_scores_codex":[0.9982617,0.0001235001,0.000445847,0.0005456163,0.0003648748,0.0002584942],"domain_scores_gemma":[0.9989733,0.0002609561,0.0001476672,0.000482185,0.00006913082,0.00006673605],"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":[0.0000990215,0.0001977561,0.000002286267,0.000006927191,0.00001767309,0.000003373341,0.002148405,0.7005555,0.01111439,0.02674992,0.0001857745,0.2589189],"study_design_scores_gemma":[0.00004939768,0.000199548,0.00001275995,0.00001141496,0.00001832342,0.00001568286,0.0007593874,0.8891237,0.08469989,0.02331312,0.001579552,0.0002171782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004786534,0.00001486835,0.9912754,0.0005539001,0.002430395,0.0004860478,0.0002133456,0.0001604221,0.00007909266],"genre_scores_gemma":[0.9633323,0.000007543553,0.0359486,0.0001330257,0.00007201467,0.0002834916,0.00006895373,0.00001514054,0.0001389368],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9585457,"threshold_uncertainty_score":0.8647337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1225793929469675,"score_gpt":0.3446536909603573,"score_spread":0.2220742980133898,"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."}}