{"id":"W2741386817","doi":"10.18653/v1/e17-2088","title":"A Dataset for Multi-Target Stance Detection","year":2017,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Focus (optics); Task (project management); Artificial intelligence; Machine learning; Joint (building); Position (finance); Product (mathematics); Artificial neural network; Position paper; Data mining; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007317519,0.001750028,0.0008050508,0.002799743,0.001212849,0.0009891589,0.001882883,0.002664856,0.01011557],"category_scores_gemma":[0.003384031,0.0002924939,0.0009582137,0.003210428,0.0003677599,0.001035014,0.001129838,0.001717732,0.01472224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001167986,"about_ca_system_score_gemma":0.001482435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01148147,"about_ca_topic_score_gemma":0.0389184,"domain_scores_codex":[0.9989277,0.0001500638,0.0001540552,0.0002633455,0.0003770495,0.0001278575],"domain_scores_gemma":[0.9978543,0.0004069146,0.0003139497,0.000410933,0.0007462995,0.0002676035],"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":[0.0004681643,0.0004926584,0.007746009,0.0009487395,0.00009920129,0.0003913958,0.0001527754,0.00142934,0.00451234,0.001763333,0.9406211,0.04137491],"study_design_scores_gemma":[0.0005400124,0.0002991623,0.0403114,0.0002919745,0.0001010122,0.001129247,0.000717074,0.01479151,0.008370467,0.003539845,0.9297762,0.0001320982],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01992098,0.0009306569,0.002715556,0.0006639868,0.0003206961,0.0003699577,0.9641514,0.00202684,0.008899831],"genre_scores_gemma":[0.01632732,0.0002295625,0.007409882,0.0002018517,0.0000646898,0.0003821936,0.9721274,0.00008509244,0.003171916],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01148147,"threshold_uncertainty_score":0.03383994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.094901962189394,"score_gpt":0.3601421386808776,"score_spread":0.2652401764914836,"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."}}