{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002390832,0.0001548413,0.0001399664,0.0003017248,0.0003125866,0.0004403919,0.000758279,0.00005084504,0.0005223796],"category_scores_gemma":[0.001224027,0.0001618242,0.00008851523,0.0003587859,0.00009401874,0.0001199102,0.0001709563,0.0002018165,0.0006260067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006480315,"about_ca_system_score_gemma":0.0001287022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001355371,"about_ca_topic_score_gemma":0.000003326087,"domain_scores_codex":[0.9981731,0.00007567129,0.0003848276,0.000451396,0.0007245983,0.0001904339],"domain_scores_gemma":[0.996955,0.0002867272,0.0002207473,0.0002254173,0.002220281,0.00009181636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009502215,0.00007344177,0.001780169,0.000001365501,0.0000688461,0.00000651661,0.0004602603,0.01387535,0.00004252797,0.9763016,0.001480948,0.005899515],"study_design_scores_gemma":[0.0002895004,0.0001099871,0.007022573,0.00002848289,0.000008248462,0.000003818248,0.0001305672,0.9045191,0.00006357,0.076565,0.01106721,0.0001919463],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005025818,0.000006466611,0.8222085,0.002053242,0.003435778,0.00008845972,0.000006640788,0.0002166616,0.1669585],"genre_scores_gemma":[0.9243928,0.00000730432,0.07194726,0.0004458211,0.001330966,0.000006523671,0.0001255092,0.000009444323,0.001734342],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.919367,"threshold_uncertainty_score":0.8046258,"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."}}