{"id":"W2035282759","doi":"10.1016/j.camwa.2009.01.001","title":"Lexical acquisition and clustering of word senses to conceptual lexicon construction","year":2009,"lang":"en","type":"article","venue":"Computers & Mathematics with Applications","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Mahidol University","keywords":"Lexicon; Word (group theory); Mathematics; Cluster analysis; Natural language processing; Lexical item; Linguistics; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002046446,0.0005990669,0.0008802028,0.003237642,0.001886819,0.005397546,0.002044941,0.00117066,0.009327925],"category_scores_gemma":[0.02178669,0.001411267,0.001506212,0.003610182,0.002732122,0.007643664,0.003389471,0.002613779,0.002574991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001728119,"about_ca_system_score_gemma":0.002300689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005344254,"about_ca_topic_score_gemma":0.007720097,"domain_scores_codex":[0.9969921,0.001237572,0.0002050656,0.0009758625,0.000320614,0.0002687974],"domain_scores_gemma":[0.9902993,0.005735918,0.0004069596,0.001588231,0.001619838,0.0003497748],"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":[0.0006691826,0.0005101784,0.01295912,0.0008445187,0.0001963955,0.0006120121,0.007215059,0.01092003,0.05583932,0.3529337,0.01101876,0.5462817],"study_design_scores_gemma":[0.00007351769,0.0001972147,0.01712612,0.0002371736,0.0002171514,0.0009419162,0.004432822,0.1879331,0.04691659,0.7174676,0.02428301,0.0001737945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2333861,0.0007578115,0.7399068,0.000794105,0.0002572099,0.0003160142,0.0007507452,0.001849666,0.02198149],"genre_scores_gemma":[0.7229014,0.0005951737,0.2650851,0.0001979385,0.0001087348,0.0002994188,0.002624662,0.000991106,0.007196425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009327925,"threshold_uncertainty_score":0.031205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01187976461111452,"score_gpt":0.2627749960082452,"score_spread":0.2508952313971307,"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."}}