{"id":"W2405306037","doi":"","title":"Columbia NLP: Sentiment Slot Filling.","year":2013,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Polarity (international relations); Sentiment analysis; Natural language processing; Artificial intelligence; Computer science; Subjectivity; Philosophy; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001632746,0.002617563,0.001467607,0.004854253,0.002512008,0.003030113,0.002396447,0.00211884,0.1463863],"category_scores_gemma":[0.00920515,0.001598707,0.001593345,0.005185896,0.0008001911,0.004634476,0.003761886,0.002746526,0.1146216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001328936,"about_ca_system_score_gemma":0.003752295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01211144,"about_ca_topic_score_gemma":0.01555007,"domain_scores_codex":[0.9984031,0.000319673,0.0001776908,0.0004383599,0.0005213984,0.0001397638],"domain_scores_gemma":[0.9978924,0.001060829,0.0001386386,0.0003263984,0.0004754625,0.0001063293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002161279,0.00006393855,0.000903768,0.002193346,0.00008074901,0.0005021906,0.0004046544,0.0007136659,0.004577012,0.01089511,0.8833112,0.09613828],"study_design_scores_gemma":[0.0002361139,0.00003776251,0.002204534,0.0004291512,0.00007978406,0.0006622678,0.000428197,0.02332116,0.009024477,0.03352188,0.9299369,0.0001177417],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.004549346,0.001630677,0.1862748,0.002151979,0.001067155,0.0009693587,0.4761483,0.2549495,0.07225885],"genre_scores_gemma":[0.0424929,0.001309862,0.246384,0.0009855704,0.0003659996,0.002480913,0.6530269,0.02084742,0.03210645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1463863,"threshold_uncertainty_score":0.4897109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008689956435664586,"score_gpt":0.2345230311793091,"score_spread":0.2258330747436445,"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."}}