{"id":"W2964166731","doi":"10.18653/v1/p19-1128","title":"Graph Neural Networks with Generated Parameters for Relation Extraction","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":176,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Relationship extraction; Artificial neural network; Artificial intelligence; Graph; Relation (database); Generator (circuit theory); Machine learning; Natural language processing; Data mining; Theoretical computer science; Power (physics)","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":[0.0009021169,0.001352157,0.0006303063,0.002589645,0.0005226142,0.001429426,0.001458127,0.0020145,0.01804227],"category_scores_gemma":[0.008475043,0.000671794,0.001103207,0.003118905,0.0003698392,0.002864921,0.001042637,0.002307969,0.01037826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071341,"about_ca_system_score_gemma":0.00103289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009643019,"about_ca_topic_score_gemma":0.01476458,"domain_scores_codex":[0.9992257,0.000204128,0.00005214521,0.0003360219,0.0001227202,0.00005925169],"domain_scores_gemma":[0.9982761,0.0009527815,0.00006834014,0.0004389361,0.0002312413,0.00003264089],"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":[0.0006095367,0.0002258275,0.00245588,0.0004722839,0.0002211755,0.0001747533,0.0001647568,0.1401103,0.0111478,0.01364228,0.04991848,0.7808568],"study_design_scores_gemma":[0.00004609526,0.00003701626,0.0008387576,0.00005607168,0.00008028421,0.00007322751,0.00004183921,0.9574389,0.006182518,0.02543284,0.009753826,0.00001860799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04254036,0.002119593,0.8875768,0.001043413,0.0004208386,0.0004041733,0.01542146,0.03777051,0.01270291],"genre_scores_gemma":[0.4867924,0.001214384,0.4594091,0.0003616193,0.0002399771,0.0006422808,0.03235065,0.002923632,0.01606582],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01804227,"threshold_uncertainty_score":0.06035739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04244054077691106,"score_gpt":0.2637783346447335,"score_spread":0.2213377938678225,"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."}}