{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002612625,0.002171428,0.00105495,0.007623209,0.002079335,0.001705812,0.003926252,0.00289292,0.007899724],"category_scores_gemma":[0.01163561,0.0006885159,0.001772254,0.006956703,0.0008277759,0.004588405,0.002495326,0.002659612,0.01074141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002230679,"about_ca_system_score_gemma":0.002949641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01561921,"about_ca_topic_score_gemma":0.03109075,"domain_scores_codex":[0.9948571,0.0008882221,0.0008928183,0.001397379,0.001710639,0.0002539225],"domain_scores_gemma":[0.9899603,0.003286449,0.0009416822,0.003290903,0.001981853,0.0005387093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000539166,0.000765791,0.01246894,0.003047232,0.0001977371,0.001017595,0.0007273633,0.005772845,0.01356995,0.01167353,0.8533418,0.09687813],"study_design_scores_gemma":[0.0003868384,0.0003037743,0.02884689,0.0004218681,0.0001366668,0.001771247,0.00096689,0.04777291,0.02448812,0.01053805,0.8841552,0.000211517],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03324377,0.001686618,0.0267625,0.001096813,0.0002882319,0.0007238426,0.9002241,0.02404597,0.01192815],"genre_scores_gemma":[0.01410836,0.0001812849,0.03365224,0.0001979682,0.00003430215,0.0004493148,0.9493703,0.0003483533,0.001657958],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01561921,"threshold_uncertainty_score":0.03105658,"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."}}