{"id":"W6996533661","doi":"","title":"A schema & constraint-based representation to understanding natural language","year":2010,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parsing; Ambiguity; Semantics (computer science); Natural language; Schema (genetic algorithms); Syntax; Consistency (knowledge bases); Interpretation (philosophy); Natural language understanding; Backtracking","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001592092,0.0005122449,0.000398538,0.001348289,0.0008147443,0.002864334,0.002393642,0.001039891,0.005658675],"category_scores_gemma":[0.004480685,0.0005301362,0.001418279,0.002911096,0.002284982,0.005881991,0.001313752,0.002160179,0.001096654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001601955,"about_ca_system_score_gemma":0.002023886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01252816,"about_ca_topic_score_gemma":0.01140419,"domain_scores_codex":[0.9992697,0.0003009333,0.00006609311,0.0001393506,0.0001865164,0.00003738982],"domain_scores_gemma":[0.9989636,0.000473108,0.00009256752,0.0002393821,0.0001890983,0.00004225209],"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":[0.00002018276,0.00002961342,0.0002803984,0.0001940697,0.00003786296,0.0001719016,0.0009983865,0.01783008,0.001352184,0.909341,0.005590883,0.06415354],"study_design_scores_gemma":[0.00002575484,0.00002868268,0.0003155815,0.0001998111,0.00005015336,0.0002740646,0.0005210886,0.2153387,0.003075975,0.679783,0.1003483,0.0000388151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002979766,0.0006992612,0.9874895,0.00179839,0.00005413328,0.00007943133,0.0003455065,0.0004143252,0.00613957],"genre_scores_gemma":[0.04607084,0.001221347,0.9482922,0.0003242622,0.00005624073,0.0002315798,0.0009978429,0.000128333,0.002677312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01252816,"threshold_uncertainty_score":0.02491045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609539966006225,"score_gpt":0.233252532195362,"score_spread":0.2171571325352998,"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."}}