{"id":"W2966381300","doi":"10.48550/arxiv.1907.12697","title":"Dual-FOFE-net Neural Models for Entity Linking with PageRank","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Recurrent neural network; Convolutional neural network; ENCODE; Task (project management); Forgetting; Dual (grammatical number); PageRank; Artificial neural network; Ranking (information retrieval); Cluster analysis; Machine learning; Theoretical computer science","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":[0.0009737752,0.001111569,0.0006962005,0.001634861,0.0004673215,0.001191022,0.002068182,0.001333566,0.005235672],"category_scores_gemma":[0.003240613,0.0004821653,0.0007397044,0.001639068,0.0004449441,0.003267202,0.0008841146,0.001633304,0.002495694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001017104,"about_ca_system_score_gemma":0.0007291617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00638452,"about_ca_topic_score_gemma":0.01308916,"domain_scores_codex":[0.9996299,0.00008985559,0.00002568069,0.0001402209,0.0000711973,0.00004311992],"domain_scores_gemma":[0.9991682,0.0003753364,0.0000916659,0.0001621507,0.0001706883,0.00003197805],"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":[0.0001645418,0.0001684313,0.0011443,0.0001341172,0.0001035116,0.0001592973,0.00007742686,0.6029678,0.002805648,0.0209184,0.01063174,0.3607248],"study_design_scores_gemma":[0.000004769704,0.00001305799,0.00007053123,0.000005014071,0.000007974932,0.0000157952,0.000003509036,0.9903301,0.0008186867,0.007813188,0.0009124456,0.00000486608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0308819,0.001253409,0.9535254,0.0004899121,0.000207542,0.00009723067,0.001042708,0.006502626,0.005999258],"genre_scores_gemma":[0.6188586,0.001065747,0.346137,0.0003670777,0.0002909384,0.0003057222,0.005346731,0.0004439451,0.02718406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00638452,"threshold_uncertainty_score":0.01751506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09641080250186186,"score_gpt":0.1867604368446303,"score_spread":0.09034963434276842,"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."}}