{"id":"W2944617758","doi":"10.1111/cogs.12730","title":"The Role of Negative Information in Distributional Semantic Learning","year":2019,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Topic Modeling","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Word2vec; Computer science; Word embedding; Artificial intelligence; Context (archaeology); Natural language processing; Word (group theory); Representation (politics); Semantics (computer science); Machine learning; Embedding; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0007473732,0.0000433862,0.00005399839,0.0000794204,0.0001545958,0.0001008764,0.0005055471,0.00001276229,0.000003947887],"category_scores_gemma":[0.0007613083,0.00003236024,0.00001483464,0.0006670925,0.0002227097,0.00144596,0.0002250113,0.000110002,0.00006208821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003900527,"about_ca_system_score_gemma":0.000181327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002199372,"about_ca_topic_score_gemma":0.000002744438,"domain_scores_codex":[0.9991559,0.00003479882,0.0001482393,0.0001390117,0.0003468432,0.000175161],"domain_scores_gemma":[0.9991908,0.0002904338,0.00009114237,0.0001149704,0.0002873351,0.0000253388],"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":[0.00001797439,0.00003174017,0.1313004,0.00001391098,0.000005275002,8.154443e-7,0.008122411,0.003599216,0.006799588,0.4405535,0.000002202363,0.409553],"study_design_scores_gemma":[0.0002572264,0.00004459663,0.1038945,0.0000677389,9.387463e-7,0.00000260277,0.002066009,0.8566003,0.01853196,0.01827722,0.0001697129,0.00008711181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7505229,0.00003024953,0.2425932,0.0001105545,0.0001171049,0.000148485,0.000001565086,0.00001638326,0.00645963],"genre_scores_gemma":[0.9992494,0.000002948288,0.0006887309,0.00002668217,0.000004868839,0.00000454619,8.324133e-7,6.292747e-7,0.00002133562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8530012,"threshold_uncertainty_score":0.1319612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007649209568656575,"score_gpt":0.2372489986065929,"score_spread":0.2295997890379363,"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."}}