{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002918012,0.0003622371,0.0004126537,0.0002150392,0.0001738689,0.0002504318,0.001648703,0.0002895595,0.000006709399],"category_scores_gemma":[0.00001088335,0.000384,0.0002237999,0.0002730612,0.00005535435,0.0008050011,0.001812734,0.0005938148,0.0000201841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001549249,"about_ca_system_score_gemma":0.0002151382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001653513,"about_ca_topic_score_gemma":0.0000638888,"domain_scores_codex":[0.9975799,0.00008718829,0.000223184,0.001479735,0.0001447614,0.0004851719],"domain_scores_gemma":[0.9975299,0.000130137,0.0002768368,0.001703249,0.0002229199,0.0001369604],"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":[0.00002751673,0.000031582,0.000706488,0.0001331351,0.0000593239,0.0001072985,0.0002072029,0.8967372,0.000007748737,0.1012733,0.00004322364,0.0006660263],"study_design_scores_gemma":[0.0006234148,0.00005673913,0.0001130612,0.0001161767,0.00005830719,0.00000734612,0.00001890623,0.9383336,0.00002884116,0.06001471,0.0001866336,0.0004422545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.209437,0.00004277339,0.7878274,0.00009594696,0.0007226021,0.0005545778,0.00001404392,0.0002316313,0.001074017],"genre_scores_gemma":[0.9791527,0.00003185178,0.01874469,0.0001542026,0.0001396724,0.000002575413,0.00002530067,0.00002756242,0.001721479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7697157,"threshold_uncertainty_score":0.9998612,"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."}}