{"id":"W2579005319","doi":"","title":"Generalizing between form and meaning using learned verb classes","year":2011,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Language Development and Disorders","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Verb; Computer science; Meaning (existential); Generalization; Linguistics; Reflexive verb; Artificial intelligence; Language acquisition; Natural language processing; Representation (politics); Psychology; Cognitive science; Modal verb; Philosophy; Epistemology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001986092,0.0003764273,0.0003712178,0.0004521268,0.0002565244,0.001618243,0.0008048816,0.0008312169,0.001747999],"category_scores_gemma":[0.01362531,0.0005376183,0.0008627303,0.0003319242,0.001848459,0.003178762,0.001156225,0.001254431,0.0002309084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009836513,"about_ca_system_score_gemma":0.0006376606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005692558,"about_ca_topic_score_gemma":0.007033722,"domain_scores_codex":[0.9989086,0.0004156121,0.00004609201,0.0003764608,0.0001724028,0.00008076911],"domain_scores_gemma":[0.9959943,0.002605962,0.0003755668,0.000681866,0.0002158773,0.0001264773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004161371,0.0004802575,0.1312957,0.0003500738,0.0003315599,0.0008394768,0.008138346,0.3291566,0.09211577,0.204697,0.001788373,0.2303907],"study_design_scores_gemma":[0.00005577419,0.0002591494,0.05509781,0.00008747574,0.0000819235,0.0004384102,0.0008581388,0.6394537,0.01207699,0.2885562,0.002925532,0.0001089716],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7520195,0.00006500456,0.2392909,0.0003167365,0.000009901539,0.00005587126,0.0001887802,0.0003584602,0.007694816],"genre_scores_gemma":[0.9709265,0.0000672248,0.02784605,0.00004442004,0.000003017974,0.0000305719,0.0002338508,0.00006548074,0.0007828884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005692558,"threshold_uncertainty_score":0.01131886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06766723877055496,"score_gpt":0.2715825069451036,"score_spread":0.2039152681745486,"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."}}