{"id":"W2962852556","doi":"","title":"Testing APSyn against Vector Cosine on Similarity Estimation","year":2016,"lang":"en","type":"preprint","venue":"Waseda University Repository (Waseda University)","topic":"Topic Modeling","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Atomic Energy of Canada Limited; University of Oxford; University of Wisconsin-Madison","keywords":"Cosine similarity; Similarity (geometry); Relevance (law); Weighting; Context (archaeology); Word (group theory); Metric (unit); Computer science; Measure (data warehouse); Intersection (aeronautics); Similarity measure; Vector space model; Artificial intelligence; Trigonometric functions; Semantic similarity; Estimation; Pattern recognition (psychology); Task (project management); Mathematics; Natural language processing; Data mining; Image (mathematics)","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":[0.03004057,0.002435808,0.002338748,0.005737766,0.001344152,0.003345137,0.003367864,0.003420115,0.004408792],"category_scores_gemma":[0.1280428,0.0004746479,0.001387514,0.005314289,0.002239282,0.01009416,0.005923463,0.002359006,0.002723614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001136943,"about_ca_system_score_gemma":0.001721675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004078396,"about_ca_topic_score_gemma":0.003050818,"domain_scores_codex":[0.959202,0.02341653,0.003031754,0.005664831,0.007689006,0.0009959368],"domain_scores_gemma":[0.8963286,0.08080933,0.003267831,0.01154622,0.006239257,0.001808831],"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":[0.009468916,0.002107397,0.1392832,0.002670998,0.003809127,0.0004527436,0.000798028,0.126231,0.006953842,0.01841401,0.03532473,0.6544859],"study_design_scores_gemma":[0.0005320146,0.004499413,0.02729315,0.0002293758,0.0004424254,0.001016271,0.001366196,0.9287319,0.008302446,0.01957859,0.007855703,0.0001524351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7901969,0.0121244,0.1607881,0.001863255,0.002030861,0.0007237211,0.006294797,0.005882504,0.0200955],"genre_scores_gemma":[0.9246397,0.0007110692,0.06364013,0.0003436826,0.000354965,0.0003196784,0.007909831,0.0003733654,0.001707544],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03004057,"threshold_uncertainty_score":0.1588716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02413957227049465,"score_gpt":0.1971345269228654,"score_spread":0.1729949546523707,"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."}}