{"id":"W1498362350","doi":"10.1007/11766247_29","title":"Sentiment Tagging of Adjectives at the Meaning Level","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Meaning (existential); Natural language processing; Artificial intelligence; Linguistics; Information retrieval; Philosophy; Epistemology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001037143,0.0003990765,0.0004968874,0.0006261863,0.0003967264,0.0003013063,0.002463984,0.0001379056,0.00004971623],"category_scores_gemma":[0.00003169382,0.0002906021,0.000242714,0.0006314936,0.0005884199,0.0003305662,0.001973392,0.0003914001,0.00002424595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002690216,"about_ca_system_score_gemma":0.0001914068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005478205,"about_ca_topic_score_gemma":0.00008226134,"domain_scores_codex":[0.9965514,0.00004539262,0.0006037775,0.001113784,0.001198856,0.0004867752],"domain_scores_gemma":[0.9975626,0.0004722645,0.000540406,0.001168415,0.0001883763,0.00006788428],"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":[0.00001014153,0.00008005134,0.0009122051,0.00006530857,0.0001446791,0.00005893828,0.004313308,0.2989684,0.00285697,0.05788281,0.0007114155,0.6339958],"study_design_scores_gemma":[0.0004000902,0.0001214763,0.0007517943,0.0006923211,0.00004501032,0.00004161271,0.0000016591,0.933074,0.02590976,0.03434065,0.003721192,0.0009004421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002565339,0.0006676735,0.9872263,0.0006395844,0.0008701042,0.0002050025,0.000003887829,0.00005022454,0.01008069],"genre_scores_gemma":[0.4687471,0.0000837468,0.5200354,0.00181858,0.0009546019,0.00001387661,0.00002402325,0.00007706226,0.008245575],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6341056,"threshold_uncertainty_score":0.9999546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02919862407214409,"score_gpt":0.2574771449464802,"score_spread":0.2282785208743361,"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."}}