{"id":"W2604207308","doi":"10.1609/aaai.v31i1.10973","title":"Disambiguating Spatial Prepositions Using Deep Convolutional Networks","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Feature (linguistics); Natural language processing; Word (group theory); Deep learning; Set (abstract data type); Feature engineering; Pattern recognition (psychology); Linguistics","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":[0.0004849877,0.001314231,0.0005225036,0.001768452,0.0006350366,0.0009630228,0.000911904,0.0007496254,0.003990125],"category_scores_gemma":[0.001688953,0.0003874877,0.0007197717,0.001322592,0.0005866144,0.003198971,0.001360858,0.001171221,0.00245531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000631078,"about_ca_system_score_gemma":0.001133074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008521514,"about_ca_topic_score_gemma":0.0170659,"domain_scores_codex":[0.999499,0.00008401643,0.00005399054,0.0002081728,0.00008119523,0.00007366054],"domain_scores_gemma":[0.9992234,0.0002619234,0.0001067855,0.000151541,0.0002143197,0.00004220438],"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":[0.0008664638,0.0003211338,0.01837751,0.0008600878,0.0001925577,0.001792701,0.001060694,0.03422627,0.07082111,0.01317841,0.03226105,0.8260421],"study_design_scores_gemma":[0.0001017927,0.0002408082,0.0156375,0.0002088313,0.0002544587,0.001481862,0.001640349,0.7610595,0.1137824,0.03169736,0.07375954,0.0001355434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4978754,0.003495028,0.4490753,0.001082965,0.0007802949,0.0002349994,0.01110863,0.01278406,0.0235633],"genre_scores_gemma":[0.7770318,0.0006808658,0.1997989,0.0002640695,0.00009316789,0.00006806311,0.01430318,0.0002560763,0.007503709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008521514,"threshold_uncertainty_score":0.01694387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1385655047278296,"score_gpt":0.3622023285665965,"score_spread":0.2236368238387669,"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."}}