{"id":"W2115791615","doi":"","title":"Deep Learning for NLP (without Magic)","year":2012,"lang":"en","type":"article","venue":"Meeting of the Association for Computational Linguistics","topic":"Topic Modeling","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Feature engineering; Deep learning; Artificial neural network; Paraphrase; Machine learning; Sentiment analysis; Natural language processing; Feature (linguistics); Focus (optics); MAGIC (telescope); Language model","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001386031,0.00008897814,0.0001465913,0.00004466897,0.0002715792,0.00005278375,0.000402146,0.00006137528,5.789303e-7],"category_scores_gemma":[0.01366101,0.00007997827,0.0001360499,0.0001205166,0.00001115577,0.00005187264,0.0001186465,0.00009763151,0.000003059626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000156419,"about_ca_system_score_gemma":0.00005087542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006018312,"about_ca_topic_score_gemma":0.000001318756,"domain_scores_codex":[0.9988171,0.00007260493,0.0003410196,0.0001474626,0.0003618822,0.0002598563],"domain_scores_gemma":[0.996572,0.001635076,0.0005976237,0.0001409311,0.00101508,0.00003930101],"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":[0.000004978131,0.00004159337,0.08693471,0.00004713894,0.0000416298,1.527839e-8,0.0007317339,0.6599267,0.00001894471,0.2508814,0.0002900635,0.001081114],"study_design_scores_gemma":[0.0003144084,0.00001799103,0.005148931,0.00002381664,0.0000259264,3.424852e-7,0.00001970639,0.9410582,0.0001378555,0.03734311,0.01581066,0.00009906648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00284011,0.00006412678,0.9922226,0.0003184541,0.001948096,0.0002811218,0.000005569278,0.00006671975,0.002253204],"genre_scores_gemma":[0.6895633,4.095321e-7,0.3092426,0.00008589859,0.000628208,0.00001066278,0.00001033502,0.00000916903,0.0004493714],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6867232,"threshold_uncertainty_score":0.9946473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02272081690193958,"score_gpt":0.2786095360606215,"score_spread":0.2558887191586819,"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."}}