{"id":"W2814750830","doi":"","title":"Learning Emotion-enriched Word Representations","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Similarity (geometry); Word (group theory); Natural language processing; Computer science; Meaning (existential); Affect (linguistics); Representation (politics); Artificial intelligence; Emotion classification; Contrast (vision); Psychology; Cognitive psychology; Linguistics; Communication","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":[0.0003559147,0.001087455,0.0004566773,0.0009804579,0.0002021738,0.0007337651,0.0008016798,0.0008409607,0.002251875],"category_scores_gemma":[0.002343219,0.0001913078,0.0006801471,0.0009462062,0.0002773841,0.00197978,0.000851773,0.001110612,0.001029467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004050607,"about_ca_system_score_gemma":0.0003287043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007891998,"about_ca_topic_score_gemma":0.001271988,"domain_scores_codex":[0.9996631,0.00007301661,0.00002543163,0.000149539,0.00004599991,0.00004300805],"domain_scores_gemma":[0.9995515,0.0001704393,0.0000561393,0.0000774562,0.0001232963,0.00002111691],"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":[0.0004972965,0.0004099227,0.003740117,0.0003405162,0.0001845087,0.0002501267,0.0004701553,0.05683611,0.05256256,0.01055502,0.01361941,0.8605343],"study_design_scores_gemma":[0.00005847109,0.000204254,0.002228771,0.00004566032,0.0001229497,0.000142651,0.0002250731,0.945376,0.01427963,0.03312405,0.004159208,0.00003319436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1988266,0.001042444,0.7910687,0.0005123278,0.0003239597,0.0001537268,0.00141587,0.002979294,0.003677069],"genre_scores_gemma":[0.7955788,0.0007608095,0.1935095,0.0002394317,0.000192072,0.0002780951,0.004787473,0.0001525785,0.004501216],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002251875,"threshold_uncertainty_score":0.007533252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05774088466866577,"score_gpt":0.3536746629688343,"score_spread":0.2959337783001685,"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."}}