{"id":"W2407223942","doi":"","title":"Discourse Relation Recognition by Comparing Various Units of Sentence Expression with Recursive Neural Network","year":2015,"lang":"en","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Atomic Energy of Canada Limited; Institute for Catastrophic Loss Reduction; University of Pennsylvania","keywords":"Sentence; Computer science; Relation (database); Meaning (existential); Natural language processing; Feature (linguistics); Artificial intelligence; Recurrent neural network; Expression (computer science); Word (group theory); Artificial neural network; Speech recognition; Linguistics; Psychology; Data mining","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":[0.001216625,0.0009483769,0.0005484988,0.001649855,0.000380531,0.001048179,0.001123801,0.0005430021,0.001947923],"category_scores_gemma":[0.004960739,0.0002521629,0.0005132075,0.00114809,0.0005131828,0.002228693,0.0007438121,0.0007909897,0.0006733718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005964751,"about_ca_system_score_gemma":0.0006048782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003098019,"about_ca_topic_score_gemma":0.003511091,"domain_scores_codex":[0.9984397,0.000496343,0.0001504006,0.0005306628,0.0002964319,0.00008629994],"domain_scores_gemma":[0.998576,0.0006307475,0.0002264338,0.0001480327,0.000377098,0.00004168491],"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":[0.0004246029,0.0001738407,0.005259444,0.0003049337,0.00012044,0.0001918729,0.001286957,0.01203052,0.1095601,0.007493346,0.002282867,0.8608712],"study_design_scores_gemma":[0.00004672716,0.0002952619,0.01118554,0.00006651937,0.0001765782,0.0002114298,0.0005394527,0.8820424,0.08515041,0.01368932,0.006502259,0.00009417718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1308485,0.0005939226,0.8598067,0.0002285486,0.00009565726,0.0002072917,0.000389618,0.003863897,0.003966045],"genre_scores_gemma":[0.5537011,0.0002623661,0.4422793,0.00008853873,0.00005549369,0.00024233,0.0009530614,0.0001796383,0.00223809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003098019,"threshold_uncertainty_score":0.006516397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03381629410901837,"score_gpt":0.2725006151502351,"score_spread":0.2386843210412167,"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."}}