{"id":"W2171086879","doi":"10.48550/arxiv.1406.2710","title":"A Multiplicative Model for Learning Distributed Text-Based Attribute Representations","year":2014,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Computer science; Natural language processing; Word (group theory); Artificial intelligence; Variety (cybernetics); Similarity (geometry); Sentence; Context (archaeology); Multiplicative function; Linguistics; Mathematics","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":[0.00304528,0.0009986764,0.001327754,0.001423,0.0005394794,0.001578432,0.003262429,0.001704626,0.002367115],"category_scores_gemma":[0.01280869,0.0008185562,0.001566098,0.001920955,0.001354696,0.004896183,0.001991991,0.002964614,0.0009861684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001144371,"about_ca_system_score_gemma":0.0008237502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001891994,"about_ca_topic_score_gemma":0.002676558,"domain_scores_codex":[0.9982127,0.0006706416,0.0001111513,0.0005961056,0.0002907527,0.0001186845],"domain_scores_gemma":[0.9941517,0.004187433,0.0004076693,0.0006316509,0.0004533049,0.0001682685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003821217,0.0003577843,0.004572275,0.0002995183,0.0002760701,0.0002519748,0.0007158823,0.5824197,0.009748959,0.2140155,0.004454558,0.1825057],"study_design_scores_gemma":[0.00001663214,0.00003828233,0.0001955377,0.000007978946,0.00002023312,0.00003347487,0.0000128212,0.9432532,0.0006759447,0.05516671,0.0005661108,0.00001303168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01504273,0.0001467518,0.983556,0.0003272244,0.00004222869,0.00004635526,0.0001680548,0.0002730446,0.0003975147],"genre_scores_gemma":[0.6553217,0.0005939704,0.331293,0.0005968045,0.0003919092,0.0007843737,0.001622691,0.0002321667,0.009163469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003262429,"threshold_uncertainty_score":0.01610518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0939435152693148,"score_gpt":0.2156646839342437,"score_spread":0.1217211686649289,"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."}}