{"id":"W2027857109","doi":"10.3115/1620853.1620921","title":"Quadratic features and deep architectures for chunking","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Quadratic equation; Generalization; Computer science; Logistic regression; Chunking (psychology); Artificial intelligence; Machine learning; Feature engineering; Quadratic programming; Feature (linguistics); Pattern recognition (psychology); Algorithm; Deep learning; Mathematics; Mathematical optimization","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":[0.002855509,0.001416773,0.0009158602,0.0006196806,0.0005531592,0.001214806,0.001712112,0.00171659,0.006167448],"category_scores_gemma":[0.01244303,0.0005445277,0.0007213455,0.0008976891,0.0007369547,0.005566672,0.001440749,0.002995495,0.00131691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008973663,"about_ca_system_score_gemma":0.0009537639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006785599,"about_ca_topic_score_gemma":0.008077859,"domain_scores_codex":[0.9991382,0.0002297302,0.00007054656,0.0002544966,0.0001733678,0.0001335478],"domain_scores_gemma":[0.9944625,0.003342547,0.0002368767,0.001086075,0.0006235332,0.0002484214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003013012,0.001050314,0.005831781,0.0007611264,0.0003028801,0.0002647801,0.0003359129,0.601404,0.01765694,0.02320728,0.02134538,0.3248267],"study_design_scores_gemma":[0.0001312273,0.0004001799,0.0006555631,0.00002911343,0.00003763658,0.0000390558,0.00003436071,0.9725631,0.007478988,0.01706035,0.001543315,0.0000271454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5319329,0.002527469,0.4299659,0.003852726,0.0007806421,0.0004565813,0.002854441,0.01189385,0.01573554],"genre_scores_gemma":[0.8525279,0.0003868022,0.1396533,0.0003961174,0.0000882094,0.0002268997,0.00212965,0.0003574059,0.004233625],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.006785599,"threshold_uncertainty_score":0.02063215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009635036367745604,"score_gpt":0.2526397629431815,"score_spread":0.2430047265754359,"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."}}