{"id":"W2950851846","doi":"10.18653/v1/n19-1323","title":"Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Relation (database); Computer science; Relationship extraction; Computational linguistics; Artificial intelligence; Natural language processing; Association (psychology); Artificial neural network; Linguistics; Volume (thermodynamics); Knowledge extraction; Cognitive science; Information extraction; Psychology; Epistemology; Philosophy; 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":[0.001408659,0.001321368,0.001200144,0.0044142,0.0008279525,0.002605473,0.002301667,0.00179115,0.004830329],"category_scores_gemma":[0.006905384,0.0008327148,0.0019902,0.005267059,0.0008171623,0.007596428,0.003103802,0.002804098,0.002913833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001183399,"about_ca_system_score_gemma":0.001022275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006410357,"about_ca_topic_score_gemma":0.01016457,"domain_scores_codex":[0.9989535,0.0003006265,0.0001006674,0.0003678287,0.0001485622,0.0001288752],"domain_scores_gemma":[0.9976888,0.001382463,0.0001237251,0.0004708861,0.0002489045,0.00008523906],"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":[0.0005111094,0.0003988327,0.002551061,0.0003194938,0.0003280696,0.0003945841,0.0004560587,0.02865106,0.009718217,0.0172048,0.02611108,0.9133558],"study_design_scores_gemma":[0.00006513311,0.0001005656,0.001366985,0.000109891,0.0002781405,0.0002140343,0.0003336622,0.828061,0.008692292,0.1515616,0.009163911,0.00005283526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07643474,0.00672457,0.8922732,0.003198903,0.0004138293,0.00023357,0.003328694,0.009731015,0.007661515],"genre_scores_gemma":[0.6598229,0.002716557,0.3168119,0.0006604184,0.0005165127,0.0003463189,0.01033758,0.0004652588,0.008322429],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006410357,"threshold_uncertainty_score":0.01615906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04272856396996166,"score_gpt":0.3311605949007924,"score_spread":0.2884320309308308,"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."}}