{"id":"W2251026829","doi":"10.3115/v1/p15-2046","title":"A Lexicalized Tree Kernel for Open Information Extraction","year":2015,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Institute of Informatics; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Computer science; Kernel (algebra); Information extraction; Natural language processing; Artificial intelligence; Tree (set theory); Joint (building); Computational linguistics; Mathematics; Engineering; Discrete mathematics; Combinatorics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001449488,0.0006133535,0.001362563,0.004273431,0.0009426608,0.002654121,0.001598177,0.001053616,0.00826508],"category_scores_gemma":[0.006594593,0.0005569256,0.001343718,0.004635875,0.000564732,0.005467902,0.003391419,0.001483005,0.007393304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006572216,"about_ca_system_score_gemma":0.00154588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002207384,"about_ca_topic_score_gemma":0.003401439,"domain_scores_codex":[0.9984087,0.0002737904,0.0002503277,0.0003880254,0.0004979262,0.0001812056],"domain_scores_gemma":[0.9970031,0.0008595953,0.0001825965,0.0008596494,0.0009616711,0.0001333702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004253372,0.0003039722,0.001233665,0.0003051747,0.0001074371,0.0002375266,0.000237077,0.004403763,0.01552444,0.0382416,0.02337628,0.9156037],"study_design_scores_gemma":[0.0001313159,0.0001767645,0.001809388,0.0001356718,0.0001501541,0.000614045,0.0003953435,0.7096797,0.03339067,0.1986345,0.05479296,0.0000894452],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007722787,0.0005532119,0.9766071,0.000134776,0.0001108484,0.0001252679,0.001037766,0.01197456,0.00173369],"genre_scores_gemma":[0.1389412,0.0004978303,0.8461261,0.0001411022,0.0001307097,0.000248493,0.006485435,0.001494093,0.005935111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00826508,"threshold_uncertainty_score":0.02764946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05184732558618997,"score_gpt":0.3581817321675291,"score_spread":0.3063344065813391,"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."}}