{"id":"W2398614220","doi":"","title":"Overview of the TAC2013 Knowledge Base Population Evaluation: English 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":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Variety (cybernetics); Polarity (international relations); Computer science; Task (project management); Knowledge base; Population; Base (topology); Entity linking; Track (disk drive); Natural language processing; Artificial intelligence; Information retrieval; Engineering; Mathematics","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":[0.01303845,0.002461527,0.001566473,0.009491207,0.00223648,0.004153801,0.004016831,0.002259027,0.0137552],"category_scores_gemma":[0.03453271,0.0005846694,0.001285141,0.006147801,0.0006224004,0.004969375,0.002843002,0.001961363,0.01037346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002793901,"about_ca_system_score_gemma":0.004475445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03036102,"about_ca_topic_score_gemma":0.03183066,"domain_scores_codex":[0.9888703,0.004132239,0.0009092552,0.001397629,0.00400323,0.0006874433],"domain_scores_gemma":[0.9856943,0.004894758,0.0003701749,0.001536319,0.006811546,0.000692864],"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":[0.0008395379,0.001649255,0.005400991,0.001452344,0.0004354576,0.0002081195,0.0004282619,0.01000027,0.007388036,0.001775227,0.2986969,0.6717256],"study_design_scores_gemma":[0.001063774,0.002142296,0.02759275,0.001555579,0.001244197,0.0008949251,0.002673955,0.4337209,0.05422841,0.0113454,0.4631514,0.0003863338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2169512,0.02154522,0.3461963,0.004621554,0.003329965,0.009797626,0.1632417,0.08598532,0.1483312],"genre_scores_gemma":[0.2259917,0.003762664,0.3274704,0.002207216,0.0005390613,0.005072256,0.398811,0.003651253,0.03249452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03036102,"threshold_uncertainty_score":0.06895471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02827833491277492,"score_gpt":0.293756293748324,"score_spread":0.265477958835549,"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."}}