{"id":"W1852496846","doi":"10.1111/tops.12211","title":"The Latent Structure of Dictionaries","year":2016,"lang":"en","type":"article","venue":"Topics in Cognitive Science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; University of Ottawa","funders":"","keywords":"Rest (music); Word (group theory); Computer science; Core (optical fiber); Set (abstract data type); Categorization; Vertex (graph theory); Artificial intelligence; Natural language processing; Graph; Mathematics; 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":[0.001083125,0.0004519052,0.0006305832,0.00273329,0.00134439,0.005114004,0.001534033,0.001036237,0.01073248],"category_scores_gemma":[0.01508943,0.0008727317,0.001056789,0.001999307,0.005070048,0.01220414,0.003724312,0.001608162,0.00204911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001875404,"about_ca_system_score_gemma":0.001318952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003526358,"about_ca_topic_score_gemma":0.003267112,"domain_scores_codex":[0.997305,0.0006687423,0.0002509047,0.001018992,0.0005397015,0.0002166112],"domain_scores_gemma":[0.9919883,0.003389966,0.0009131919,0.00216326,0.001092055,0.0004532352],"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":[0.0001746019,0.00006253123,0.007563639,0.0004103263,0.0000884937,0.000231983,0.004010972,0.005392462,0.005751299,0.9067855,0.003002517,0.06652559],"study_design_scores_gemma":[0.0000396137,0.00006448157,0.004780621,0.0001285832,0.00009049773,0.0003312512,0.001211014,0.04182143,0.003122045,0.9331046,0.01524869,0.0000571518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2815263,0.001346136,0.6492704,0.00378225,0.0001830744,0.0001759634,0.004420019,0.001764199,0.05753171],"genre_scores_gemma":[0.9021036,0.0005239994,0.08587225,0.0003155998,0.00008155983,0.000199643,0.003424462,0.0004111481,0.007067846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01073248,"threshold_uncertainty_score":0.03590369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01782947085291535,"score_gpt":0.3011776556249314,"score_spread":0.283348184772016,"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."}}