{"id":"W3174860526","doi":"10.18653/v1/2021.findings-acl.128","title":"Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction","year":2021,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Key Research and Development Program of China","keywords":"Computer science; Natural language processing; Artificial intelligence; Sentence; Sentiment analysis; Inference; ENCODE; Graph; Exploit; Ontology; Pipeline (software); Theoretical computer science","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":[0.0001564449,0.00008616939,0.0001343051,0.0000727537,0.00009922041,0.0002614968,0.0001121104,0.0000264682,0.0004027775],"category_scores_gemma":[0.00002244821,0.00007807905,0.00006103757,0.0003170206,0.000009216146,0.0003745408,0.0001101206,0.00005941713,0.00008354624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002131404,"about_ca_system_score_gemma":0.00002821953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001133992,"about_ca_topic_score_gemma":0.00001036872,"domain_scores_codex":[0.999068,0.00005137839,0.0001746201,0.0003499161,0.0002043417,0.0001516938],"domain_scores_gemma":[0.9994711,0.00007377422,0.00006230185,0.0002810935,0.00005119012,0.00006051747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001720453,0.0006624791,0.004462047,0.00008091043,0.0006543809,0.0002988626,0.001985005,0.0001561862,0.6440003,0.1041052,0.005905028,0.2376724],"study_design_scores_gemma":[0.000899112,0.00007192323,0.01216466,0.0000556719,0.00008645576,0.0001310907,0.00059649,0.2199293,0.758778,0.001520846,0.005308947,0.0004574979],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1214674,0.0003924612,0.8635737,0.001904173,0.000482163,0.00006744666,1.816884e-7,0.00011016,0.01200228],"genre_scores_gemma":[0.9700012,0.0001053893,0.0254728,0.0002349629,0.00004872984,0.000003365793,0.000003351728,0.000003956628,0.00412624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8485338,"threshold_uncertainty_score":0.4410132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01717425674907036,"score_gpt":0.277706360302578,"score_spread":0.2605321035535076,"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."}}