{"id":"W2115982722","doi":"","title":"Joint Training of Dependency Parsing Filters through Latent Support Vector Machines","year":2011,"lang":"en","type":"article","venue":"NPARC","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Parsing; Artificial intelligence; Dependency grammar; Classifier (UML); Pointwise; Pattern recognition (psychology); Support vector machine; Graph; Machine learning; Theoretical computer science; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004469663,0.0013674,0.00168006,0.002246478,0.0007854133,0.001757749,0.002526935,0.002286157,0.003558347],"category_scores_gemma":[0.0108209,0.0008598643,0.001428687,0.001755447,0.0007494475,0.00408205,0.001248841,0.003555971,0.002344054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005257,"about_ca_system_score_gemma":0.001909423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005073057,"about_ca_topic_score_gemma":0.007413572,"domain_scores_codex":[0.9978777,0.0006927145,0.0001440104,0.0006892167,0.0003460761,0.0002502516],"domain_scores_gemma":[0.9929381,0.004906482,0.0003761014,0.0005152933,0.001098341,0.0001657757],"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":[0.0003086734,0.0003483276,0.003291847,0.0001211605,0.000154364,0.0001206883,0.0001550269,0.1560545,0.006222018,0.01204339,0.0067451,0.8144349],"study_design_scores_gemma":[0.000007490032,0.00001853578,0.0001654837,0.000008573469,0.00001318386,0.00001046001,0.00001121781,0.9927193,0.001881564,0.004751266,0.0004063148,0.000006561181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01714638,0.0002134609,0.9784219,0.0001921761,0.00004354176,0.00004296084,0.0001405205,0.003217842,0.0005810714],"genre_scores_gemma":[0.353357,0.0002450851,0.6404589,0.0002796795,0.0001167005,0.0002617523,0.001492062,0.0003705698,0.003418335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005073057,"threshold_uncertainty_score":0.02363813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.073102836970246,"score_gpt":0.2755741511709338,"score_spread":0.2024713142006878,"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."}}