{"id":"W2006877875","doi":"10.1080/01690960902840279","title":"A computational model of learning semantic roles from child-directed language","year":2009,"lang":"en","type":"article","venue":"Language and Cognitive Processes","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Natural language processing; Verb; Predicate (mathematical logic); Ambiguity; Artificial intelligence; Semantic role labeling; Comprehension; Language acquisition; Semantics (computer science); Meaning (existential); Probabilistic logic; Event (particle physics); Linguistics; Psychology; Sentence","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":[0.001498868,0.0005629092,0.0005783135,0.0007934482,0.0005207026,0.001568195,0.002406714,0.001332368,0.003302055],"category_scores_gemma":[0.005319139,0.0008677139,0.001422366,0.0007610193,0.002251229,0.00464221,0.001312363,0.001950065,0.0005193972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001801995,"about_ca_system_score_gemma":0.001366087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005850074,"about_ca_topic_score_gemma":0.007999199,"domain_scores_codex":[0.9994178,0.0002413797,0.00002852194,0.0001417898,0.000112719,0.00005793233],"domain_scores_gemma":[0.997837,0.001609659,0.0001462528,0.0001802763,0.0001300499,0.00009680649],"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":[0.0001282066,0.0001336177,0.002950647,0.0002253609,0.000102457,0.0006347887,0.001280238,0.3241596,0.003054282,0.6176138,0.002068407,0.04764863],"study_design_scores_gemma":[0.00002766447,0.00003925919,0.000354146,0.00001699891,0.0000249415,0.0002124918,0.00004001281,0.7413486,0.0007278065,0.2551479,0.00203946,0.00002074385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04786696,0.0002784028,0.9431205,0.001155293,0.00002520268,0.00006742189,0.0002777648,0.00051665,0.006691795],"genre_scores_gemma":[0.6584454,0.0006198027,0.3320635,0.0003029372,0.00004400331,0.0004684448,0.0006701212,0.0001445974,0.007241185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005850074,"threshold_uncertainty_score":0.01307446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006790039251520495,"score_gpt":0.2534377029089888,"score_spread":0.2466476636574683,"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."}}