{"id":"W2251238100","doi":"","title":"Indexing Spoken Documents with Hierarchical Semantic Structures: Semantic Tree-to-string Alignment Models","year":2011,"lang":"en","type":"article","venue":"NPARC","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Search engine indexing; String (physics); Tree (set theory); Information retrieval; Natural language processing; Index (typography); Artificial intelligence; Semantic computing; Hierarchical database model; Semantic Web; Data mining; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002523124,0.0005720549,0.001503117,0.001634458,0.0009664245,0.002701068,0.002250182,0.001729597,0.003736576],"category_scores_gemma":[0.01485169,0.0006011701,0.001076867,0.005346143,0.001340961,0.01238466,0.00169058,0.002080619,0.001968097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001267365,"about_ca_system_score_gemma":0.001780573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004893958,"about_ca_topic_score_gemma":0.004617341,"domain_scores_codex":[0.9978424,0.000904488,0.0001792649,0.0004474111,0.0004955491,0.0001308409],"domain_scores_gemma":[0.9931044,0.004211758,0.0007014056,0.001001171,0.0008082112,0.0001730164],"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":[0.0007036465,0.0003650881,0.003263094,0.00069449,0.000164345,0.0003275087,0.001417917,0.2277975,0.009229918,0.3130099,0.01943435,0.4235923],"study_design_scores_gemma":[0.00002862151,0.0000741272,0.0003939339,0.00002214777,0.00002557081,0.0001174849,0.0001416833,0.8441151,0.00267718,0.1486284,0.003739477,0.00003633423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01473843,0.0005103596,0.9801524,0.0005145418,0.00008986108,0.00007137815,0.0004358563,0.001544709,0.001942449],"genre_scores_gemma":[0.3562664,0.001153487,0.6337322,0.0003673676,0.0003378994,0.0003818771,0.002775743,0.0005543306,0.00443068],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004893958,"threshold_uncertainty_score":0.01334369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056997363901189,"score_gpt":0.2508908526425403,"score_spread":0.2303208790035284,"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."}}