{"id":"W2971001654","doi":"10.18653/v1/d19-1451","title":"Aligning Cross-Lingual Entities with Multi-Aspect Information","year":2019,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education, India; Singapore University of Technology and Design; Compute Canada","keywords":"Computer science; Natural language processing; Joint (building); Natural (archaeology); Artificial intelligence; Engineering; History; Archaeology; Architectural engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001223625,0.0000650952,0.00006547804,0.00005923003,0.00005116588,0.0003667234,0.0003042322,0.0000244167,0.00004713474],"category_scores_gemma":[0.000009098046,0.00004978543,0.00001771862,0.00008612921,0.00001205626,0.00190164,0.0001095151,0.00005491151,0.0003195839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000201952,"about_ca_system_score_gemma":0.00004309205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009868362,"about_ca_topic_score_gemma":0.00001383528,"domain_scores_codex":[0.9994079,0.000007612295,0.0001344565,0.0001223617,0.0001813186,0.0001463158],"domain_scores_gemma":[0.9995555,0.00001816344,0.00004594496,0.000284867,0.00006790027,0.00002758133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002074759,0.00007107613,0.2215164,0.0001548066,0.00006981884,0.0000131923,0.0291105,0.06447799,0.0008871072,0.6145372,0.0003122981,0.06882875],"study_design_scores_gemma":[0.001019805,0.00008298113,0.007715039,0.00003396753,0.000002222617,0.0000208874,0.0004670377,0.9773886,0.009471914,0.0004833195,0.003034709,0.0002795838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3217323,0.00000569577,0.6654301,0.00004417602,0.0001786854,0.00007030212,2.239345e-7,0.0001483624,0.01239012],"genre_scores_gemma":[0.8426223,5.223512e-7,0.1554796,0.0002269152,0.00001997891,0.000002251193,0.00000139272,0.000002213568,0.001644821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9129105,"threshold_uncertainty_score":0.4107711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01344844880703849,"score_gpt":0.2505986682585105,"score_spread":0.237150219451472,"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."}}