{"id":"W2990684147","doi":"10.1145/3459104.3459147","title":"Relation Extraction with Synthetic Explanations and Neural Network","year":2021,"lang":"en","type":"article","venue":"2021 International Symposium on Electrical, Electronics and Information Engineering","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Relationship extraction; Relation (database); Computer science; Artificial intelligence; Sentence; Artificial neural network; Training set; Set (abstract data type); Machine learning; Natural language processing; Noise (video); Pattern recognition (psychology); Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001390776,0.001389742,0.000410462,0.001412972,0.0004167463,0.0005841163,0.001167071,0.001198167,0.002218191],"category_scores_gemma":[0.009394404,0.0003542402,0.0008413919,0.00129094,0.0005123475,0.001217057,0.0009023013,0.001001886,0.000747667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006520986,"about_ca_system_score_gemma":0.0006768616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002673735,"about_ca_topic_score_gemma":0.005967916,"domain_scores_codex":[0.9985128,0.0006502346,0.0001078838,0.0003835316,0.0002883297,0.00005722591],"domain_scores_gemma":[0.9939591,0.004477719,0.0003731473,0.0005575686,0.0005552747,0.00007729363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001433723,0.0007086637,0.01394813,0.002951548,0.0004011571,0.004620579,0.002600397,0.241965,0.05364848,0.02103014,0.05180894,0.6048833],"study_design_scores_gemma":[0.0001647791,0.0002706482,0.003907126,0.0001251617,0.0001166395,0.0007880464,0.0004733841,0.9142611,0.03555726,0.01743577,0.0268414,0.00005871528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2762908,0.002408786,0.6853288,0.001567062,0.0003709087,0.0007390208,0.01078625,0.0170314,0.005476886],"genre_scores_gemma":[0.4090787,0.0005408141,0.5616543,0.0002876609,0.0001026979,0.0006399762,0.02461713,0.0003802708,0.002698421],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002673735,"threshold_uncertainty_score":0.007420599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003798536857846377,"score_gpt":0.1909978751739409,"score_spread":0.1871993383160946,"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."}}