{"id":"W2951864354","doi":"10.18653/v1/p19-1423","title":"Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Open Text (Canada)","funders":"Biotechnology and Biological Sciences Research Council; Associazione Italiana per la Ricerca sul Cancro; National Institute of Advanced Industrial Science and Technology","keywords":"Computer science; Relationship extraction; Sentence; Pairwise comparison; Graph; Artificial intelligence; Convolutional neural network; Exploit; Natural language processing; Relation (database); Theoretical computer science; Information extraction; 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":[0.0003197072,0.0002674375,0.0002310037,0.000118318,0.0001002323,0.0002788907,0.0008444031,0.0002062962,0.00005638977],"category_scores_gemma":[0.00001316463,0.0002282481,0.0001031211,0.000174447,0.00003998417,0.0007478442,0.0008968397,0.000716791,0.00006527377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001458353,"about_ca_system_score_gemma":0.0001623149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002618466,"about_ca_topic_score_gemma":0.00006475658,"domain_scores_codex":[0.9978484,0.00009152321,0.0003872349,0.0008588097,0.0004797593,0.00033428],"domain_scores_gemma":[0.9983926,0.00009218783,0.0003137884,0.0009460001,0.0001774573,0.00007793462],"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":[0.00002512464,0.00002957052,0.01399947,0.00004872654,0.00006145127,0.000009117563,0.000156041,0.8795579,0.00002868507,0.09507178,0.001439327,0.009572782],"study_design_scores_gemma":[0.0002589292,0.00004602459,0.02239581,0.0001746985,0.00001500481,0.00004742103,0.00001377445,0.9504712,0.00001713323,0.0258596,0.0003601931,0.0003402255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01425896,0.0001058514,0.9782451,0.001111641,0.003155482,0.0004037428,0.000002330431,0.0002579691,0.00245887],"genre_scores_gemma":[0.7905519,0.00001714681,0.2068047,0.0002618754,0.0002849159,0.00003056246,0.00003083047,0.00001229516,0.002005785],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.776293,"threshold_uncertainty_score":0.9307687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04059959707883033,"score_gpt":0.2682899030591384,"score_spread":0.227690305980308,"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."}}