{"id":"W2082229537","doi":"10.7202/019924ar","title":"A simple and robust method for extracting terminology","year":2009,"lang":"en","type":"article","venue":"Meta Journal des traducteurs","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Simple (philosophy); Terminology; Process (computing); Lexicon; Natural language processing; Set (abstract data type); Porting; Artificial intelligence; Word (group theory); Domain (mathematical analysis); Context (archaeology); Information retrieval; Software; Linguistics; Programming language; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002398561,0.00166768,0.001354878,0.01054623,0.001774813,0.003344501,0.00180324,0.00163827,0.01048344],"category_scores_gemma":[0.01053538,0.000832112,0.00189771,0.005969291,0.0009951849,0.004288672,0.002997837,0.001832399,0.01628519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007154949,"about_ca_system_score_gemma":0.003020924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001335261,"about_ca_topic_score_gemma":0.002150059,"domain_scores_codex":[0.9957765,0.0007612986,0.0007216593,0.001005113,0.001555567,0.0001800104],"domain_scores_gemma":[0.9947431,0.001455637,0.0004285137,0.001305482,0.001951683,0.0001155432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006622618,0.00006042419,0.001076677,0.001085812,0.0001168946,0.0004381062,0.0008213979,0.001299116,0.1023978,0.02781371,0.02386793,0.840956],"study_design_scores_gemma":[0.0001314553,0.0002681266,0.006036108,0.0005592042,0.0003809294,0.004997126,0.00148315,0.07255895,0.1468782,0.08851153,0.6776802,0.0005151269],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002488029,0.0005651328,0.9871793,0.0002121518,0.0002532448,0.0003333503,0.001130866,0.00428293,0.003554899],"genre_scores_gemma":[0.01046985,0.0003411712,0.981761,0.000106737,0.000107858,0.0002901422,0.002755795,0.0006970739,0.003470312],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01054623,"threshold_uncertainty_score":0.0350706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05540557279115226,"score_gpt":0.3390912007655686,"score_spread":0.2836856279744163,"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."}}