{"id":"W2953129863","doi":"10.48550/arxiv.cs/0508103","title":"Corpus-based Learning of Analogies and Semantic Relations","year":2005,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Natural language processing; Computer science; Artificial intelligence; Linguistics; Corpus linguistics; Philosophy","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":[0.003777261,0.001355325,0.001132658,0.005097138,0.001422748,0.002404526,0.003175625,0.002256967,0.005601095],"category_scores_gemma":[0.02834474,0.0008881815,0.001396033,0.00353038,0.001889602,0.006148285,0.001994071,0.002846621,0.002012262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001763386,"about_ca_system_score_gemma":0.001917106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007119993,"about_ca_topic_score_gemma":0.01034178,"domain_scores_codex":[0.9959911,0.001483786,0.0002628294,0.001377184,0.0007787571,0.0001063708],"domain_scores_gemma":[0.9857717,0.009997031,0.0004734528,0.001940831,0.00165683,0.0001602545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000446253,0.0006801892,0.008442196,0.0007783455,0.0002998954,0.0004023218,0.0008441575,0.1105093,0.006262782,0.04234389,0.02730976,0.801681],"study_design_scores_gemma":[0.0001115692,0.000106172,0.002112492,0.0001049786,0.00007851278,0.0001984059,0.0002618349,0.9076672,0.004908605,0.06820342,0.01619625,0.00005065725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09823782,0.002049471,0.8724402,0.001339359,0.0004271036,0.0005259695,0.003623769,0.007681207,0.01367508],"genre_scores_gemma":[0.415979,0.0005723754,0.5652172,0.0003928074,0.0002222651,0.0009857208,0.01112832,0.0004267581,0.00507547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007119993,"threshold_uncertainty_score":0.01997632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02907103913200881,"score_gpt":0.2852280818143401,"score_spread":0.2561570426823312,"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."}}