{"id":"W4366967907","doi":"10.1109/wi-iat55865.2022.00046","title":"Entity Level QA Pairs Dataset for Sentiment Analysis","year":2022,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Sentiment analysis; Task (project management); Character (mathematics); Series (stratigraphy); Information retrieval; Span (engineering); Natural language processing; Artificial intelligence; Gauge (firearms); 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":[0.001011764,0.001662208,0.0007009426,0.003456341,0.000906368,0.00100685,0.001185068,0.001236003,0.01126073],"category_scores_gemma":[0.003580347,0.0002452334,0.001108885,0.002648699,0.0002472876,0.001646839,0.001417071,0.001093983,0.01389607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009702154,"about_ca_system_score_gemma":0.0008545085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008467915,"about_ca_topic_score_gemma":0.01481918,"domain_scores_codex":[0.9987539,0.0002867262,0.0001947191,0.0002575971,0.0003708744,0.0001362324],"domain_scores_gemma":[0.9984724,0.0003142601,0.0001783918,0.0003127294,0.0005740202,0.000148127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005538007,0.0003803463,0.007656983,0.001550949,0.000144994,0.0003527757,0.0003453923,0.002022388,0.009712765,0.003586385,0.9277281,0.04596528],"study_design_scores_gemma":[0.0003094247,0.000318109,0.03724639,0.0002462727,0.00009567675,0.0006740408,0.0009850138,0.03015158,0.009854728,0.005352146,0.91465,0.0001166188],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01956862,0.0007308652,0.004303205,0.000566672,0.0002502987,0.0005517965,0.962138,0.00483274,0.007057885],"genre_scores_gemma":[0.01785799,0.0001474489,0.00783634,0.0001455192,0.00006125951,0.000428287,0.9713212,0.0001148016,0.0020872],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01126073,"threshold_uncertainty_score":0.03767085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08130547624460353,"score_gpt":0.2964838357407647,"score_spread":0.2151783594961612,"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."}}