{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001275337,0.0001169481,0.00009237343,0.000114441,0.0001158193,0.0003025219,0.0001209085,0.00005632724,0.000008830454],"category_scores_gemma":[0.00004451313,0.0001149413,0.00002128544,0.0003203083,0.000006171624,0.001801389,0.00004095355,0.0002377726,0.00000377691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001253371,"about_ca_system_score_gemma":0.00005116273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003112187,"about_ca_topic_score_gemma":0.000002070015,"domain_scores_codex":[0.999071,0.0000159233,0.0002363923,0.000190103,0.0002650254,0.0002215397],"domain_scores_gemma":[0.9994822,0.00009322271,0.0000820651,0.0001457406,0.0001356576,0.00006109848],"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":[0.00001069264,0.00002173034,0.0001843737,0.000008752032,0.00004518893,0.000003885283,0.0001408064,0.5203742,0.001102816,0.4511762,0.00007585607,0.02685557],"study_design_scores_gemma":[0.0002217933,0.00008046069,0.0007256321,0.00002013218,0.000006547496,0.0001296674,0.000005148806,0.9840361,0.0005657068,0.0002521942,0.01382298,0.0001336717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03851977,0.000403173,0.9543523,0.004125683,0.0003698884,0.000126393,0.000001476712,0.0001079929,0.001993293],"genre_scores_gemma":[0.9907792,0.000852402,0.00781179,0.0003156285,0.0001087616,0.00002395909,0.00003776854,0.000007877575,0.0000626125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9522594,"threshold_uncertainty_score":0.4687171,"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."}}