{"id":"W3029096252","doi":"","title":"A Lexicon-Based Approach for Detecting Hedges in Informal Text","year":2020,"lang":"en","type":"article","venue":"Language Resources and Evaluation","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Hedge; Lexicon; Natural language processing; Sentence; Interview; Artificial intelligence; Part-of-speech tagging; Linguistics; Part of speech; Sociology","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":[0.0007021911,0.00009185033,0.0001108299,0.00008488895,0.00007442343,0.0001599223,0.0002395379,0.00006043341,0.000003710014],"category_scores_gemma":[0.000339241,0.00007815137,0.00002796807,0.0002637224,0.00001756144,0.0002726742,0.00007236953,0.0001026568,6.81299e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002010383,"about_ca_system_score_gemma":0.00004046077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000381691,"about_ca_topic_score_gemma":0.00001174061,"domain_scores_codex":[0.9991583,0.00005648395,0.00015932,0.0002387404,0.0002245405,0.0001626053],"domain_scores_gemma":[0.9995987,0.00008936315,0.00008083451,0.0001269159,0.00005498562,0.00004918831],"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":[0.00005172665,0.00002183918,0.0007332789,0.0002427819,0.000006037357,0.000002654209,0.03508713,0.001717164,0.006821169,0.0006721347,0.00003592095,0.9546081],"study_design_scores_gemma":[0.0005279567,0.0000895242,0.0002757241,0.00002601118,0.000006467237,0.00000226209,0.0008388279,0.9882621,0.009349185,0.0003765325,0.0001285212,0.0001169378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.270407,0.004515802,0.7232941,0.00072614,0.00001520456,0.0004940091,0.000001600896,0.0002294827,0.0003166431],"genre_scores_gemma":[0.7355359,0.00000128697,0.2638507,0.0004824114,0.00005422658,0.0000575627,0.000007724808,0.000005239978,0.000004902719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9865449,"threshold_uncertainty_score":0.3186921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03137950489801756,"score_gpt":0.300703666162043,"score_spread":0.2693241612640255,"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."}}