{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002790744,0.00006145315,0.0001152891,0.00004622536,0.0001553283,0.0001556447,0.0002833501,0.00002346636,0.0002267306],"category_scores_gemma":[0.000007096058,0.00006220103,0.00003647037,0.0002293012,0.0001057447,0.0002302707,0.0001060367,0.00003493387,0.0000574367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003984192,"about_ca_system_score_gemma":0.00001234164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007031213,"about_ca_topic_score_gemma":0.000003082082,"domain_scores_codex":[0.9994009,0.0000410233,0.0001805726,0.0001757128,0.0001005472,0.0001012667],"domain_scores_gemma":[0.9993246,0.0001228644,0.00009247704,0.0003334544,0.00008213508,0.00004451998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000001081709,0.00003521567,0.0004683122,0.000008647543,0.0000255288,4.641922e-8,0.0003579828,0.000009076406,0.001019735,0.9613357,0.000715329,0.0360233],"study_design_scores_gemma":[0.0002934789,0.00005975651,0.005009962,0.00001446765,0.00004767845,0.000004054645,0.001374095,0.005365611,0.02073181,0.9370255,0.02977039,0.0003031345],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08829089,0.0008585465,0.9005558,0.0004893647,0.00005717577,0.0004033505,0.000002857445,0.00009416793,0.009247785],"genre_scores_gemma":[0.991115,0.00005828029,0.005762662,0.0000775954,0.00003651516,0.0001418551,0.000008028801,0.000003759748,0.002796262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9028242,"threshold_uncertainty_score":0.2536484,"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."}}