{"id":"W1956103381","doi":"","title":"Automatic Acquisition of Lexical Formality","year":2010,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Formality; Computer science; Word (group theory); Natural language processing; Artificial intelligence; Word Association; Metric (unit); Similarity (geometry); Association (psychology); Task (project management); Synonym (taxonomy); Linguistics; Speech recognition; Psychology","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":[0.0002495009,0.0001219978,0.0001334041,0.0001590228,0.00006324582,0.0001331121,0.0009412477,0.00008486478,0.0001598763],"category_scores_gemma":[0.0009964185,0.0001155447,0.00005689657,0.0001325527,0.00009273527,0.0001200518,0.0001563586,0.0003017429,0.00002515264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003112158,"about_ca_system_score_gemma":0.0001684038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001207357,"about_ca_topic_score_gemma":0.000003494185,"domain_scores_codex":[0.9986449,0.00002663892,0.0003531397,0.0002376491,0.000611286,0.0001264143],"domain_scores_gemma":[0.9977387,0.0002136832,0.0002203217,0.0002226588,0.001544081,0.00006055503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000006627933,0.00007935549,0.0001428719,0.00001160592,0.00001148441,0.000005897304,0.00006275532,0.0001377114,0.0006442547,0.9895677,0.0001557307,0.009174014],"study_design_scores_gemma":[0.0001165086,0.00004592767,0.0008053833,0.00003452084,0.000002413144,0.000009297484,0.000002944721,0.4795916,0.002243363,0.5167366,0.0003159817,0.00009540895],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01659489,0.00001203467,0.9594651,0.001306226,0.002318448,0.0001241879,0.00004270903,0.0003816561,0.01975469],"genre_scores_gemma":[0.6036465,6.272828e-7,0.3959264,0.0001970688,0.0001677271,0.000003989855,0.0000329827,0.000003787684,0.00002094859],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5870515,"threshold_uncertainty_score":0.4711776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0284122819659165,"score_gpt":0.3393629900576921,"score_spread":0.3109507080917756,"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."}}