{"id":"W831928984","doi":"","title":"Exploiting a Multilingual Web-based Encyclopedia for Bilingual Terminology Extraction","year":2010,"lang":"en","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Terminology; Encyclopedia; Natural language processing; Rank (graph theory); Information retrieval; Exploit; Artificial intelligence; Ontology; Filter (signal processing); Term (time); Information extraction; World Wide Web; Linguistics","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":[0.001331081,0.001251913,0.0009869721,0.0166811,0.001266226,0.002553696,0.0007045711,0.0004808957,0.006010252],"category_scores_gemma":[0.006555955,0.0005139925,0.0008500471,0.01189769,0.0004279181,0.004175402,0.00218057,0.000708042,0.004788286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006610636,"about_ca_system_score_gemma":0.002988437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003848104,"about_ca_topic_score_gemma":0.007435271,"domain_scores_codex":[0.9987131,0.0003125033,0.0003614204,0.0003132579,0.0002257528,0.00007393023],"domain_scores_gemma":[0.9971584,0.001151761,0.0002819146,0.0003650386,0.000922002,0.000120896],"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":[0.0003533464,0.0002515715,0.00892714,0.003264825,0.0003011069,0.00249745,0.002016248,0.002973321,0.1090944,0.01954703,0.01819306,0.8325806],"study_design_scores_gemma":[0.000274698,0.0008232248,0.05197795,0.001825148,0.001498956,0.009060941,0.007471228,0.1079918,0.2132771,0.02887394,0.5763076,0.0006173629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1146035,0.004003382,0.8140163,0.0009207589,0.0003693738,0.00151705,0.0226511,0.01125289,0.03066571],"genre_scores_gemma":[0.1378144,0.001870542,0.8152937,0.0001606322,0.0001411608,0.0008182406,0.03847826,0.0009594383,0.004463533],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0166811,"threshold_uncertainty_score":0.02010632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890596775989091,"score_gpt":0.3136890455180256,"score_spread":0.2947830777581347,"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."}}