{"id":"W2250974997","doi":"10.63317/29jpe4q7e5co","title":"Improving Open Relation Extraction via Sentence Re-Structuring","year":2014,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Relationship extraction; Structuring; Natural language processing; Information extraction; Artificial intelligence; Predicate (mathematical logic); Sentence; Natural language; Open domain; Exploit; Natural language understanding; Relation (database); Context (archaeology); Information retrieval; Data mining; Question answering; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002135475,0.001768252,0.001484401,0.003062342,0.0008772062,0.00160184,0.001403732,0.0009646891,0.004146322],"category_scores_gemma":[0.009723631,0.0005332588,0.001401466,0.002536479,0.0005114804,0.004066529,0.002045391,0.001763187,0.00445682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003661922,"about_ca_system_score_gemma":0.00127732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001227566,"about_ca_topic_score_gemma":0.002077391,"domain_scores_codex":[0.9977242,0.0007197376,0.0002923134,0.0005845106,0.0005867424,0.00009244491],"domain_scores_gemma":[0.9905499,0.005498899,0.0006902043,0.001296907,0.001847278,0.0001168712],"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":[0.0003132492,0.0002948177,0.001959528,0.0008738959,0.0001460868,0.0006034907,0.001515195,0.005150421,0.1137432,0.005776119,0.01934888,0.8502752],"study_design_scores_gemma":[0.0001895375,0.0007503393,0.008932943,0.0002808108,0.0009457369,0.002393951,0.00236798,0.4650305,0.316867,0.04172279,0.1602276,0.0002907197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04234566,0.001559088,0.9378889,0.0005612462,0.0002932716,0.0005756978,0.0017738,0.01202529,0.002977123],"genre_scores_gemma":[0.09648589,0.0006814173,0.8898863,0.0002525072,0.0002980687,0.0002569724,0.007538787,0.0009265348,0.003673535],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004146322,"threshold_uncertainty_score":0.01387078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02503818307054018,"score_gpt":0.2652567600072647,"score_spread":0.2402185769367245,"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."}}