{"id":"W2108711363","doi":"","title":"A Strategy of Mapping Polish WordNet onto Princeton WordNet","year":2012,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"WordNet; Premise; Computer science; Set (abstract data type); Natural language processing; Focus (optics); Artificial intelligence; Lexical database; Range (aeronautics); Information retrieval; Linguistics; Programming language; Philosophy","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":[0.00155462,0.0007445642,0.000419479,0.003804877,0.001896846,0.002192403,0.0009552566,0.0005701733,0.007832087],"category_scores_gemma":[0.006650538,0.000815952,0.0007725426,0.002735085,0.001270356,0.005428575,0.004388485,0.001652013,0.003417654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007372301,"about_ca_system_score_gemma":0.001706074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004162129,"about_ca_topic_score_gemma":0.006140627,"domain_scores_codex":[0.9989713,0.0003190777,0.000104873,0.000315042,0.0002188019,0.00007083003],"domain_scores_gemma":[0.9986494,0.0002553635,0.00006167174,0.0006309948,0.0003364067,0.00006613094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001356405,0.0001806922,0.002893368,0.0002623186,0.00009163954,0.0004149135,0.002980398,0.004939711,0.01456842,0.5718862,0.01295955,0.3886872],"study_design_scores_gemma":[0.00006288382,0.0002736166,0.003636281,0.0001799987,0.000112112,0.0008693518,0.002502028,0.06601118,0.0472357,0.5755864,0.3033997,0.0001307208],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02082188,0.00008530088,0.9531497,0.0007366914,0.0001743856,0.0005412287,0.0008688683,0.002520006,0.02110184],"genre_scores_gemma":[0.15013,0.0002834449,0.8307717,0.0003863994,0.00004632485,0.00156565,0.002044608,0.0009836874,0.01378834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007832087,"threshold_uncertainty_score":0.02620089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06179235534177949,"score_gpt":0.3410952297029126,"score_spread":0.2793028743611331,"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."}}