{"id":"W2168138178","doi":"10.5296/ijl.v3i1.648","title":"Machine Learning for Automatic Labeling of Frames and Frame Elements in Text","year":2011,"lang":"en","type":"article","venue":"International Journal of Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"FrameNet; Computer science; Frame (networking); Task (project management); Natural language processing; Artificial intelligence; Representation (politics); Semantics (computer science); Programming language; Engineering; Parsing","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":[0.0004568711,0.00006345256,0.000136276,0.0002331016,0.00001644173,0.00004041682,0.0005583833,0.00003922749,0.000006222258],"category_scores_gemma":[0.004030654,0.00005475653,0.00003297522,0.00006887035,0.00002333511,0.0000807423,0.000110058,0.0002009575,2.222931e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003008958,"about_ca_system_score_gemma":0.00004430805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002274866,"about_ca_topic_score_gemma":0.000002998592,"domain_scores_codex":[0.999118,0.00002076896,0.0004572329,0.00007510822,0.0002475847,0.00008128523],"domain_scores_gemma":[0.998433,0.0001717202,0.000487628,0.0000583274,0.0008215829,0.00002776167],"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":[0.0001951615,0.0006577586,0.1689152,0.0003623107,0.0004347795,0.0003888722,0.01445088,0.0001984713,0.006469363,0.4143715,0.0001968247,0.3933589],"study_design_scores_gemma":[0.001898711,0.0007662377,0.002337921,0.001631943,0.00004380911,0.0001582377,0.0001302295,0.41396,0.02999987,0.545293,0.003425074,0.0003549465],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03445251,0.003492997,0.9599165,0.0001773232,0.001325583,0.000114336,0.000006187585,0.00005643059,0.0004581803],"genre_scores_gemma":[0.5086825,0.00003017173,0.4911692,0.00003759401,0.00006894812,5.020722e-7,4.296343e-7,0.000003204711,0.000007457322],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.47423,"threshold_uncertainty_score":0.4825361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02305452834565917,"score_gpt":0.3157570860943017,"score_spread":0.2927025577486425,"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."}}