{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004233973,0.001275375,0.0005861502,0.002128631,0.0004483539,0.0007522452,0.001094216,0.0009646992,0.00164318],"category_scores_gemma":[0.001314934,0.0004157693,0.001042257,0.002583069,0.000296199,0.001904042,0.0007820476,0.001218203,0.00160942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001089854,"about_ca_system_score_gemma":0.001079236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0130937,"about_ca_topic_score_gemma":0.0292211,"domain_scores_codex":[0.999617,0.00005489806,0.00002838959,0.0001636034,0.00008977263,0.00004640544],"domain_scores_gemma":[0.9994993,0.0001633438,0.00007617216,0.000108317,0.0001286362,0.00002413115],"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":[0.0003931814,0.0003037477,0.004874401,0.0005542611,0.0003393042,0.0007940211,0.0003399831,0.09011935,0.0609474,0.01136127,0.03579934,0.7941738],"study_design_scores_gemma":[0.00002056172,0.00005819182,0.003427369,0.00003373959,0.0001169747,0.0001529814,0.0000575199,0.9511562,0.01930368,0.01580093,0.009841054,0.0000307332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08300694,0.002890038,0.8846659,0.0006101045,0.0001934122,0.0002620403,0.005630214,0.01694561,0.005795791],"genre_scores_gemma":[0.5213899,0.001348254,0.4382921,0.0003702161,0.000130917,0.0002876192,0.02594865,0.0007103676,0.01152205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0130937,"threshold_uncertainty_score":0.02603501,"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."}}